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Guoping Qiu
College of Electronics and Information Engineering, Guangdong Key Laboratory for Intelligent Information Processing, and Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen University, Shenzhen 518060, China, and also with the School of Computer Science, University of Nottingham, Nottingham NG8 1BB, U.K..

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Journal article
Published: 26 April 2021 in IEEE Transactions on Neural Networks and Learning Systems
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Class imbalance is a prevalent phenomenon in various real-world applications and it presents significant challenges to model learning, including deep learning. In this work, we embed ensemble learning into the deep convolutional neural networks (CNNs) to tackle the class-imbalanced learning problem. An ensemble of auxiliary classifiers branching out from various hidden layers of a CNN is trained together with the CNN in an end-to-end manner. To that end, we designed a new loss function that can rectify the bias toward the majority classes by forcing the CNN's hidden layers and its associated auxiliary classifiers to focus on the samples that have been misclassified by previous layers, thus enabling subsequent layers to develop diverse behavior and fix the errors of previous layers in a batch-wise manner. A unique feature of the new method is that the ensemble of auxiliary classifiers can work together with the main CNN to form a more powerful combined classifier, or can be removed after finished training the CNN and thus only acting the role of assisting class imbalance learning of the CNN to enhance the neural network's capability in dealing with class-imbalanced data. Comprehensive experiments are conducted on four benchmark data sets of increasing complexity (CIFAR-10, CIFAR-100, iNaturalist, and CelebA) and the results demonstrate significant performance improvements over the state-of-the-art deep imbalance learning methods.

ACS Style

Zhi Chen; Jiang Duan; Li Kang; Guoping Qiu. Class-Imbalanced Deep Learning via a Class-Balanced Ensemble. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -15.

AMA Style

Zhi Chen, Jiang Duan, Li Kang, Guoping Qiu. Class-Imbalanced Deep Learning via a Class-Balanced Ensemble. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-15.

Chicago/Turabian Style

Zhi Chen; Jiang Duan; Li Kang; Guoping Qiu. 2021. "Class-Imbalanced Deep Learning via a Class-Balanced Ensemble." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-15.

Journal article
Published: 29 October 2020 in IEEE Transactions on Image Processing
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Fog removal from an image is an active research topic in computer vision. However, current literature is weak in the following two areas which in many ways are hindering progress for developing defogging algorithms. First, there is no true real-world and naturally occurring foggy image datasets suitable for developing defogging models. Second, there is no suitable mathematically simple and easy to use image quality assessment (IQA) methods for evaluating the visual quality of defogged images. We address these two aspects in this paper. We first introduce a new foggy image dataset called multiple real-world foggy image dataset (MRFID). MRFID contains foggy and clear images of 200 outdoor scenes. For each scene, one clear image and 4 foggy images of different densities defined as slightly foggy, moderately foggy, highly foggy, and extremely foggy, are manually selected from images taken from these scenes over the course of one calendar year. We then process the foggy images of MRFID using 16 defogging methods to obtain 12,800 defogged images (DFIs) and perform a comprehensive subjective evaluation of the visual quality of the DFIs. Through collecting the mean opinion score (MOS) of 120 subjects and evaluating a variety of fog-relevant image features, we have developed a new Fog-relevant Feature based SIMilarity index (FRFSIM) for assessing the visual quality of DFIs. We present extensive experimental results to show that our new visual quality assessment measure, the FRFSIM, is more consistent with the MOS than other IQA methods and is therefore more suitable for evaluating defogged images than other state-of-the-art IQA methods. Our dataset and relevant code are available at http://www.vistalab.ac.cn/MRFID-for-defogging/.

ACS Style

Wei Liu; Fei Zhou; Tao Lu; Jiang Duan; Guoping Qiu. Image Defogging Quality Assessment: Real-World Database and Method. IEEE Transactions on Image Processing 2020, 30, 176 -190.

AMA Style

Wei Liu, Fei Zhou, Tao Lu, Jiang Duan, Guoping Qiu. Image Defogging Quality Assessment: Real-World Database and Method. IEEE Transactions on Image Processing. 2020; 30 (99):176-190.

Chicago/Turabian Style

Wei Liu; Fei Zhou; Tao Lu; Jiang Duan; Guoping Qiu. 2020. "Image Defogging Quality Assessment: Real-World Database and Method." IEEE Transactions on Image Processing 30, no. 99: 176-190.

Journal article
Published: 10 September 2020 in IEEE Transactions on Medical Imaging
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Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion. Although promising, using deep learning for fovea localisation also has many unsolved challenges. In this paper, we present a new end-to-end fovea localisation method based on a hierarchical coarse-to-fine deep regression neural network. The innovative features of the new method include a multi-scale feature fusion technique and a self-attention technique to exploit location, semantic, and contextual information in an integrated framework, a multi-field-of-view (multi-FOV) feature fusion technique for context-aware feature learning and a Gaussian-shift-cropping method for augmenting effective training data. We present extensive experimental results on two public databases and show that our new method achieved state-of-the-art performances. We also present a comprehensive ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various constituent components.

ACS Style

Ruitao Xie; Jingxin Liu; Rui Cao; Connor S. Qiu; Jiang Duan; Jon Garibaldi; Guoping Qiu. End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network. IEEE Transactions on Medical Imaging 2020, 40, 116 -128.

AMA Style

Ruitao Xie, Jingxin Liu, Rui Cao, Connor S. Qiu, Jiang Duan, Jon Garibaldi, Guoping Qiu. End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network. IEEE Transactions on Medical Imaging. 2020; 40 (1):116-128.

Chicago/Turabian Style

Ruitao Xie; Jingxin Liu; Rui Cao; Connor S. Qiu; Jiang Duan; Jon Garibaldi; Guoping Qiu. 2020. "End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network." IEEE Transactions on Medical Imaging 40, no. 1: 116-128.

Journal article
Published: 19 August 2020 in IEEE Transactions on Image Processing
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Deep neural networks (DNNs) have been extensively applied in image processing, including visual saliency map pre-diction of images. A major difficulty in using a DNN for visual saliency prediction is the lack of labeled ground truth of visual saliency. A powerful DNN usually contains a large number of trainable parameters. This condition can easily lead to model over-fitting. In this study, we develop a novel method that over-comes such difficulty by embedding hierarchical knowledge of existing visual saliency models in a DNN. We achieve the objective of exploiting the knowledge contained in the existing visual sali-ency models by using saliency maps generated by local, global, and semantic models to tune and fix about 92.5% of the parame-ters in our network in a hierarchical manner. As a result, the number of trainable parameters that need to be tuned by the ground truth is considerably reduced. This reduction enables us to fully utilize the power of a large DNN and overcome the issue of over-fitting at the same time. Furthermore, we introduce a simple but very effective center prior in designing the learning cost function of the DNN by attaching high importance to the errors around the image center. We also present extensive experimental results on four commonly used public databases to demonstrate the superiority of the proposed method over classical and state-of-the-art methods on various evaluation metrics.

ACS Style

Fei Zhou; Rongguo Yao; Guangsen Liao; Bozhi Liu; Guoping Qiu. Visual Saliency via Embedding Hierarchical Knowledge in a Deep Neural Network. IEEE Transactions on Image Processing 2020, 29, 8490 -8505.

AMA Style

Fei Zhou, Rongguo Yao, Guangsen Liao, Bozhi Liu, Guoping Qiu. Visual Saliency via Embedding Hierarchical Knowledge in a Deep Neural Network. IEEE Transactions on Image Processing. 2020; 29 (99):8490-8505.

Chicago/Turabian Style

Fei Zhou; Rongguo Yao; Guangsen Liao; Bozhi Liu; Guoping Qiu. 2020. "Visual Saliency via Embedding Hierarchical Knowledge in a Deep Neural Network." IEEE Transactions on Image Processing 29, no. 99: 8490-8505.

Journal article
Published: 01 January 2020 in IEEE Transactions on Image Processing
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Structure-texture image decomposition is a funda-mental but challenging topic in computational graphics and image processing. In this paper, we introduce a structure-aware and a texture-aware measures to facilitate the structure-texture de-composition (STD) of images. Edge strengths and spatial scales that have been widely-used in previous STD researches cannot describe the structures and textures of images well. The proposed two measures differentiate image textures from image structures based on their distinctive characteristics. Specifically, the first one aims to measure the anisotropy of local gradients, and the second one is designed to measure the repeatability degree of signal pat-terns in a neighboring region. Since these two measures describe different properties of image structures and textures, they are complementary to each other. The STD is achieved by optimizing an objective function based on the two new measures. As using traditional optimization methods to solve the optimization prob-lem will require designing different optimizers for different func-tional spaces, we employ an architecture of deep neural network to optimize the STD cost function in a unified manner. The ex-perimental results demonstrate that, as compared with some state-of-the-art methods, our method can better separate image structure and texture and result in shaper edges in the structural component. Furthermore, to demonstrate the usefulness of the proposed STD method, we have successfully applied it to several applications including detail enhancement, edge detection, and visual quality assessment of super-resolved images.

ACS Style

Fei Zhou; Qun Chen; Bozhi Liu; Guoping Qiu. Structure and Texture-Aware Image Decomposition via Training a Neural Network. IEEE Transactions on Image Processing 2020, 29, 3458 -3473.

AMA Style

Fei Zhou, Qun Chen, Bozhi Liu, Guoping Qiu. Structure and Texture-Aware Image Decomposition via Training a Neural Network. IEEE Transactions on Image Processing. 2020; 29 ():3458-3473.

Chicago/Turabian Style

Fei Zhou; Qun Chen; Bozhi Liu; Guoping Qiu. 2020. "Structure and Texture-Aware Image Decomposition via Training a Neural Network." IEEE Transactions on Image Processing 29, no. : 3458-3473.

Journal article
Published: 24 December 2019 in IEEE Transactions on Medical Imaging
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Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the deep learning model is computationally expensive, while resizing the original image to achieve low resolution incurs information loss. Some hard-attention based approaches have emerged to select possible lesion regions from images to avoid processing the original image. However, these hard-attention based approaches usually take a long time to converge with weak guidance, and valueless patches may be trained by the classifier. To overcome this problem, we propose a deep selective attention approach that aims to select valuable regions in the original images for classification. In our approach, a decision network is developed to decide where to crop and whether the cropped patch is necessary for classification. These selected patches are then trained by the classification network, which then provides feedback to the decision network to update its selection policy. With such a co-evolution training strategy, we show that our approach can achieve a fast convergence rate and high classification accuracy. Our approach is evaluated on a public breast cancer histopathological image database, where it demonstrates superior performance compared to state-of-the-art deep learning approaches, achieving approximately 98% classification accuracy while only taking 50% of the training time of the previous hard-attention approach.

ACS Style

Bolei Xu; Jingxin Liu; Xianxu Hou; Bozhi Liu; Jon Garibaldi; Ian O. Ellis; Andy Green; Linlin Shen; Guoping Qiu. Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification. IEEE Transactions on Medical Imaging 2019, 39, 1930 -1941.

AMA Style

Bolei Xu, Jingxin Liu, Xianxu Hou, Bozhi Liu, Jon Garibaldi, Ian O. Ellis, Andy Green, Linlin Shen, Guoping Qiu. Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification. IEEE Transactions on Medical Imaging. 2019; 39 (6):1930-1941.

Chicago/Turabian Style

Bolei Xu; Jingxin Liu; Xianxu Hou; Bozhi Liu; Jon Garibaldi; Ian O. Ellis; Andy Green; Linlin Shen; Guoping Qiu. 2019. "Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification." IEEE Transactions on Medical Imaging 39, no. 6: 1930-1941.

Journal article
Published: 28 October 2019 in Image and Vision Computing
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In the field of single image defogging, there are two main methods. One is the image restoration method based on the atmospheric scattering theory which can recover the image texture details well. The other is the image enhancement method based on Retinex theory which can improve the image contrast well. In practice, however, the former can easily lead to low contrast images; the latter is prone to losing texture details. Therefore, how to effectively combine the advantages of both to remove fog is a key issue in the field. In this paper, we have developed a physics based generative adversarial network (PBGAN) to exploit the advantages between those two methods in parallel. To our knowledge, it is the first learning defogging framework that incorporates these two methods and to enable them to work together and complement each other. Our method has two generative adversarial modules, the Contrast Enhancement (CE) module and the Texture Restoration (TR) module. To improve contrast in the CE module, we introduced a novel inversion-adversarial loss and a novel inversion-cycle consistency loss for training the generator. To improve the texture in the TR module, we introduced two convolutional neural networks to learn the atmospheric light coefficient and the transmission map, respectively. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed approach performs better than several state-of-the-art methods quantitatively and qualitatively.

ACS Style

Wei Liu; Rongguo Yao; Guoping Qiu. A physics based generative adversarial network for single image defogging. Image and Vision Computing 2019, 92, 103815 .

AMA Style

Wei Liu, Rongguo Yao, Guoping Qiu. A physics based generative adversarial network for single image defogging. Image and Vision Computing. 2019; 92 ():103815.

Chicago/Turabian Style

Wei Liu; Rongguo Yao; Guoping Qiu. 2019. "A physics based generative adversarial network for single image defogging." Image and Vision Computing 92, no. : 103815.

Journal article
Published: 13 March 2019 in Neurocomputing
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We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we also implement a generative adversarial training mechanism to force the VAE to output realistic and natural images. We present experimental results to show that the VAE trained with our new method outperforms state of the art in generating face images with much clearer and more natural noses, eyes, teeth, hair textures as well as reasonable backgrounds. We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction.

ACS Style

Xianxu Hou; Ke Sun; Linlin Shen; Guoping Qiu. Improving variational autoencoder with deep feature consistent and generative adversarial training. Neurocomputing 2019, 341, 183 -194.

AMA Style

Xianxu Hou, Ke Sun, Linlin Shen, Guoping Qiu. Improving variational autoencoder with deep feature consistent and generative adversarial training. Neurocomputing. 2019; 341 ():183-194.

Chicago/Turabian Style

Xianxu Hou; Ke Sun; Linlin Shen; Guoping Qiu. 2019. "Improving variational autoencoder with deep feature consistent and generative adversarial training." Neurocomputing 341, no. : 183-194.

Preprint
Published: 15 February 2019
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With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two publicly available remote sensing image retrieval datasets and show that our method significantly outperforms state-of-the-art.

ACS Style

Rui Cao; Qian Zhang; Jiasong Zhu; Qing Li; Qingquan Li; Bozhi Liu; Guoping Qiu. Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network. 2019, 1 .

AMA Style

Rui Cao, Qian Zhang, Jiasong Zhu, Qing Li, Qingquan Li, Bozhi Liu, Guoping Qiu. Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network. . 2019; ():1.

Chicago/Turabian Style

Rui Cao; Qian Zhang; Jiasong Zhu; Qing Li; Qingquan Li; Bozhi Liu; Guoping Qiu. 2019. "Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network." , no. : 1.

Journal article
Published: 03 January 2019 in Remote Sensing
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This paper presents a novel indoor topological localization method based on mobile phone videos. Conventional methods suffer from indoor dynamic environmental changes and scene ambiguity. The proposed Visual Landmark Sequence-based Indoor Localization (VLSIL) method is capable of addressing problems by taking steady indoor objects as landmarks. Unlike many feature or appearance matching-based localization methods, our method utilizes highly abstracted landmark sematic information to represent locations and thus is invariant to illumination changes, temporal variations, and occlusions. We match consistently detected landmarks against the topological map based on the occurrence order in the videos. The proposed approach contains two components: a convolutional neural network (CNN)-based landmark detector and a topological matching algorithm. The proposed detector is capable of reliably and accurately detecting landmarks. The other part is the matching algorithm built on the second order hidden Markov model and it can successfully handle the environmental ambiguity by fusing sematic and connectivity information of landmarks. To evaluate the method, we conduct extensive experiments on the real world dataset collected in two indoor environments, and the results show that our deep neural network-based indoor landmark detector accurately detects all landmarks and is expected to be utilized in similar environments without retraining and that VLSIL can effectively localize indoor landmarks.

ACS Style

Jiasong Zhu; Qing Li; Rui Cao; Ke Sun; Tao Liu; Jonathan M. Garibaldi; Qingquan Li; Bozhi Liu; Guoping Qiu. Indoor Topological Localization Using a Visual Landmark Sequence. Remote Sensing 2019, 11, 73 .

AMA Style

Jiasong Zhu, Qing Li, Rui Cao, Ke Sun, Tao Liu, Jonathan M. Garibaldi, Qingquan Li, Bozhi Liu, Guoping Qiu. Indoor Topological Localization Using a Visual Landmark Sequence. Remote Sensing. 2019; 11 (1):73.

Chicago/Turabian Style

Jiasong Zhu; Qing Li; Rui Cao; Ke Sun; Tao Liu; Jonathan M. Garibaldi; Qingquan Li; Bozhi Liu; Guoping Qiu. 2019. "Indoor Topological Localization Using a Visual Landmark Sequence." Remote Sensing 11, no. 1: 73.

Conference paper
Published: 08 December 2018 in Computer Vision
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The goal of this paper is to automatically recognize characters in popular TV series. In contrast to conventional approaches which rely on weak supervision afforded by transcripts, subtitles or character facial data, we formulate the problem as the multi-label classification which requires only label-level supervision. We propose a novel semantic projection network consisting of two stacked subnetworks with specially designed constraints. The first subnetwork is a contractive autoencoder which focuses on reconstructing feature activations extracted from a pre-trained single-label convolutional neural network (CNN). The second subnetwork functions as a region-based multi-label classifier which produces character labels for the input video frame as well as reconstructing the input visual feature from the mapped semantic labels space. Extensive experiments show that the proposed model achieves state-of-the-art performance in comparison with recent approaches on three challenging TV series datasets (the Big Bang Theory, the Defenders and Nirvava in Fire).

ACS Style

Ke Sun; Zhuo Lei; Jiasong Zhu; Xianxu Hou; Bozhi Liu; Guoping Qiu. Character Prediction in TV Series via a Semantic Projection Network. Computer Vision 2018, 300 -311.

AMA Style

Ke Sun, Zhuo Lei, Jiasong Zhu, Xianxu Hou, Bozhi Liu, Guoping Qiu. Character Prediction in TV Series via a Semantic Projection Network. Computer Vision. 2018; ():300-311.

Chicago/Turabian Style

Ke Sun; Zhuo Lei; Jiasong Zhu; Xianxu Hou; Bozhi Liu; Guoping Qiu. 2018. "Character Prediction in TV Series via a Semantic Projection Network." Computer Vision , no. : 300-311.

Journal article
Published: 15 November 2018 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultrahigh-resolution traffic videos taken from an unmanned aerial vehicle (UAV). We first capture nearly an hour-long ultrahigh-resolution traffic video at five busy road intersections of a modern megacity by flying a UAV during the rush hours. We then randomly sampled over 17 K 512 × 512 pixel image patches from the video frames and manually annotated over 64 K vehicles to form a dataset for this paper, which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of an advanced deep neural network (DNN) based vehicle detection and localization, type (car, bus, and truck) recognition, tracking, and vehicle counting over time. We will present extensive experimental results to demonstrate the effectiveness of our solution. We will show that our enhanced single shot multibox detector (Enhanced-SSD) outperforms other DNN-based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultrahigh-resolution video provides more information that enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultrahigh-resolution video and UAV), but also provides an advanced DNN-based solution for exploiting these technological advancements for urban traffic density estimation.

ACS Style

Jiasong Zhu; Ke Sun; Sen Jia; Qingquan Li; Xianxu Hou; Weidong Lin; Bozhi Liu; Guoping Qiu. Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 4968 -4981.

AMA Style

Jiasong Zhu, Ke Sun, Sen Jia, Qingquan Li, Xianxu Hou, Weidong Lin, Bozhi Liu, Guoping Qiu. Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (12):4968-4981.

Chicago/Turabian Style

Jiasong Zhu; Ke Sun; Sen Jia; Qingquan Li; Xianxu Hou; Weidong Lin; Bozhi Liu; Guoping Qiu. 2018. "Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 12: 4968-4981.

Conference paper
Published: 09 November 2018 in Advances in Intelligent Systems and Computing
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In this paper, we construct a multi-task deep learning model to simultaneously predict people number and the level of crowd density. Motivated by the success of applying “ambiguous labelling” to age estimation problem, we also manage to employ this strategy to the people counting problem. We show that it is a reasonable strategy since people counting problem is similar to the age estimation problem. Also, by applying “ambiguous labelling”, we are able to augment the size of training dataset, which is a desirable property when applying to deep learning model. In a series of experiment, we show that the “ambiguous labelling” strategy can not only improve the performance of deep learning but also enhance the prediction ability of traditional computer vision methods such as Random Projection Forest with hand-crafted features.

ACS Style

Bolei Xu; Wenbin Zou; Jonathan Garibaldi; Guoping Qiu. A Classification-Regression Deep Learning Model for People Counting. Advances in Intelligent Systems and Computing 2018, 136 -149.

AMA Style

Bolei Xu, Wenbin Zou, Jonathan Garibaldi, Guoping Qiu. A Classification-Regression Deep Learning Model for People Counting. Advances in Intelligent Systems and Computing. 2018; ():136-149.

Chicago/Turabian Style

Bolei Xu; Wenbin Zou; Jonathan Garibaldi; Guoping Qiu. 2018. "A Classification-Regression Deep Learning Model for People Counting." Advances in Intelligent Systems and Computing , no. : 136-149.

Proceedings article
Published: 01 October 2018 in 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
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With the rapid development of modern road traffic network, the demand of automatic traffic understanding has become a vital issue for building the intelligent traffic monitoring system and self-driving techniques. In this paper, we focus on behavior recognition of moving objects at busy road intersections in a modern city. To achieve this, we first capture a 4K (3840×2160) traffic video at a busy road intersection of a modern megacity by flying an UAV during the rush hours, and then manually annotate locations and types of road vehicles to form a dataset for this research. Next we propose an innovative behavior recognition framework consists of advanced deep neural network based vehicle detection and localization, type (car, bus and truck) recognition, tracking and behavior recognition over time. We will present experimental results to demonstrate the effectiveness of our solution. This paper not only demonstrates the advantages of using the latest technological advancements (4K video and UAV) but also provides an advanced deep neural network based solution for exploiting these technological advancements for urban traffic analysis.

ACS Style

Jiasong Zhu; Weidong Lin; Ke Sun; Xianxu Hou; Bozhi Liu; Guoping Qiu. Behavior Recognition of Moving Objects Using Deep Neural Networks. 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) 2018, 45 -52.

AMA Style

Jiasong Zhu, Weidong Lin, Ke Sun, Xianxu Hou, Bozhi Liu, Guoping Qiu. Behavior Recognition of Moving Objects Using Deep Neural Networks. 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). 2018; ():45-52.

Chicago/Turabian Style

Jiasong Zhu; Weidong Lin; Ke Sun; Xianxu Hou; Bozhi Liu; Guoping Qiu. 2018. "Behavior Recognition of Moving Objects Using Deep Neural Networks." 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) , no. : 45-52.

Journal article
Published: 27 September 2018 in Remote Sensing
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Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.

ACS Style

Rui Cao; Jiasong Zhu; Wei Tu; Qingquan Li; Jinzhou Cao; Bozhi Liu; Qian Zhang; Guoping Qiu. Integrating Aerial and Street View Images for Urban Land Use Classification. Remote Sensing 2018, 10, 1553 .

AMA Style

Rui Cao, Jiasong Zhu, Wei Tu, Qingquan Li, Jinzhou Cao, Bozhi Liu, Qian Zhang, Guoping Qiu. Integrating Aerial and Street View Images for Urban Land Use Classification. Remote Sensing. 2018; 10 (10):1553.

Chicago/Turabian Style

Rui Cao; Jiasong Zhu; Wei Tu; Qingquan Li; Jinzhou Cao; Bozhi Liu; Qian Zhang; Guoping Qiu. 2018. "Integrating Aerial and Street View Images for Urban Land Use Classification." Remote Sensing 10, no. 10: 1553.

Journal article
Published: 24 September 2018 in IEEE Access
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This paper first presents a generic geometric prior for the image processing problems. The proposed term allows each individual pixel to automatically choose its own geometric prior. This behavior is fundamentally different from traditional regularizations that use only one prior for all pixels. This term, however, is difficult to be minimized by traditional optimization methods. Therefore, we further propose an iterative image filter to impose this generic geometric prior. Moreover, this proposed filter has a neural network representation, where the kernels in our filter can be learned based on the convolutional neural network. Several numerical experiments are performed to confirm the effectiveness and efficiency of this new filter and its related neural networks.

ACS Style

Yuanhao Gong; Xianxu Hou; Fei Li; Guoping Qiu. Image Filtering With Generic Geometric Prior. IEEE Access 2018, 6, 54320 -54330.

AMA Style

Yuanhao Gong, Xianxu Hou, Fei Li, Guoping Qiu. Image Filtering With Generic Geometric Prior. IEEE Access. 2018; 6 ():54320-54330.

Chicago/Turabian Style

Yuanhao Gong; Xianxu Hou; Fei Li; Guoping Qiu. 2018. "Image Filtering With Generic Geometric Prior." IEEE Access 6, no. : 54320-54330.

Journal article
Published: 03 September 2018 in IEEE Transactions on Medical Imaging
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One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.

ACS Style

Jingxin Liu; Bolei Xu; Chi Zheng; Yuanhao Gong; Jon Garibaldi; Daniele Soria; Andew Green; Ian Ellis; Wenbin Zou; Guoping Qiu. An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA. IEEE Transactions on Medical Imaging 2018, 38, 617 -628.

AMA Style

Jingxin Liu, Bolei Xu, Chi Zheng, Yuanhao Gong, Jon Garibaldi, Daniele Soria, Andew Green, Ian Ellis, Wenbin Zou, Guoping Qiu. An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA. IEEE Transactions on Medical Imaging. 2018; 38 (2):617-628.

Chicago/Turabian Style

Jingxin Liu; Bolei Xu; Chi Zheng; Yuanhao Gong; Jon Garibaldi; Daniele Soria; Andew Green; Ian Ellis; Wenbin Zou; Guoping Qiu. 2018. "An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA." IEEE Transactions on Medical Imaging 38, no. 2: 617-628.

Journal article
Published: 06 June 2018 in Remote Sensing
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Vehicle behavior recognition is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. This paper presents an all-in-one behavior recognition framework for moving vehicles based on the latest deep learning techniques. Unlike traditional traffic analysis methods which rely on low-resolution videos captured by road cameras, we capture 4K ( 3840×2178 ) traffic videos at a busy road intersection of a modern megacity by flying a unmanned aerial vehicle (UAV) during the rush hours. We then manually annotate locations and types of road vehicles. The proposed method consists of the following three steps: (1) vehicle detection and type recognition based on deep neural networks; (2) vehicle tracking by data association and vehicle trajectory modeling; (3) vehicle behavior recognition by nearest neighbor search and by bidirectional long short-term memory network, respectively. This paper also presents experimental results of the proposed framework in comparison with state-of-the-art approaches on the 4K testing traffic video, which demonstrated the effectiveness and superiority of the proposed method.

ACS Style

Jiasong Zhu; Ke Sun; Sen Jia; Weidong Lin; Xianxu Hou; Bozhi Liu; Guoping Qiu. Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition. Remote Sensing 2018, 10, 887 .

AMA Style

Jiasong Zhu, Ke Sun, Sen Jia, Weidong Lin, Xianxu Hou, Bozhi Liu, Guoping Qiu. Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition. Remote Sensing. 2018; 10 (6):887.

Chicago/Turabian Style

Jiasong Zhu; Ke Sun; Sen Jia; Weidong Lin; Xianxu Hou; Bozhi Liu; Guoping Qiu. 2018. "Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition." Remote Sensing 10, no. 6: 887.

Journal article
Published: 01 May 2018 in IEICE Transactions on Information and Systems
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ACS Style

Xianxu Hou; Jiasong Zhu; Ke Sun; Linlin Shen; Guoping Qiu. Object Specific Deep Feature for Face Detection. IEICE Transactions on Information and Systems 2018, E101.D, 1270 -1277.

AMA Style

Xianxu Hou, Jiasong Zhu, Ke Sun, Linlin Shen, Guoping Qiu. Object Specific Deep Feature for Face Detection. IEICE Transactions on Information and Systems. 2018; E101.D (5):1270-1277.

Chicago/Turabian Style

Xianxu Hou; Jiasong Zhu; Ke Sun; Linlin Shen; Guoping Qiu. 2018. "Object Specific Deep Feature for Face Detection." IEICE Transactions on Information and Systems E101.D, no. 5: 1270-1277.

Review
Published: 01 March 2018 in 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)
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The Huff model is a well used mathematical abstraction for predicting shopping centre patronage. It considers two factors: shopping centre attractiveness, and customers' travel costs. Here, taxi trajectory data (more than three million journeys) and social media data (more than eight thousand customer reviews) is used to calibrate the Huff model for five primary shopping centres in the rapidly expanding metropolitan city of Shenzhen, China. The Huff model is calibrated in two ways: globally, to find the single pair of best-fit parameters for attractiveness and travel cost; and locally, using Geographical Weighted Regression to find the best-fit parameters at each spatial location. Results demonstrate that customer reviews on social media provide relatively high prediction accuracy for weekend shopping behaviours when the Huff model is calibrated globally. In contrast, customer footfall, calculated directly from number of taxi journeys, provides higher prediction accuracy when the Huff model is calibrated locally. This suggests that, at weekends, sensitivity to footfall has greater spatial variance (i.e., customers living in some areas have greater preference for shopping at popular centres) than sensitivity to customer reviews (i.e., regardless of where customers live, positive reviews on social media are equally likely to affect behaviour). We present this geographical homogeneity in review sensitivity and heterogeneity in footfall sensitivity as a novel discovery with potential applications in urban, retail, and transportation planning.

ACS Style

Shuhui Gong; John Cartlidge; Ruibin Bai; Yang Yue; Qingquan Li; Guoping Qiu. Automated prediction of shopping behaviours using taxi trajectory data and social media reviews. 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA) 2018, 117 -121.

AMA Style

Shuhui Gong, John Cartlidge, Ruibin Bai, Yang Yue, Qingquan Li, Guoping Qiu. Automated prediction of shopping behaviours using taxi trajectory data and social media reviews. 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA). 2018; ():117-121.

Chicago/Turabian Style

Shuhui Gong; John Cartlidge; Ruibin Bai; Yang Yue; Qingquan Li; Guoping Qiu. 2018. "Automated prediction of shopping behaviours using taxi trajectory data and social media reviews." 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA) , no. : 117-121.