Among a number of ensemble learning techniques, boosting and bagging are the most popular sampling-based ensemble approaches for classification problems. Boosting is considered stronger than bagging on noise-free data set with complex class structures, whereas bagging is more robust than boosting in cases where noise data are present. In this paper, we extend both ensemble approaches to clustering tasks, and propose a novel hybrid sampling-based clustering ensemble by combining the strengths of boosting and bagging. In our approach, the input partitions are iteratively generated via a hybrid process inspired by both boosting and bagging. Then, a novel consensus function is proposed to encode the local and global cluster structure of input partitions into a single representation, and applies a single clustering algorithm to such representation to obtain the consolidated consensus partition. Our approach has been evaluated on 2-D-synthetic data, collection of benchmarks, and real-world facial recognition data sets, which show that the proposed technique outperforms the existing benchmarks for a variety of clustering tasks.

Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of image-based salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets.

Being able to predict the salient object is of fundamental importance in image processing and computer vision. With numerous approaches proposed for automatic image and video salient object detection, much less work has been dedicated to detecting and segmenting salient objects from light fields. In this paper, based on the intrinsic characteristics of light fields, we carefully explore the complementary coherence among multiple cues including spatial, edge and depth information, and elaborately design a multi-task collaborative network for light field salient object detection. More specifically, the correlation mechanisms among edge detection, depth inference and salient object detection are carefully investigated to facilitate the representative saliency features. We first model the coherence among low-level features and heuristic semantic priors, as well as the edge information. Subsequently, the depth-oriented saliency features are derived from the geometry of light fields, in which the 3D convolution operation is leveraged with powerful representation capability to model the disparity correlations among multiple viewpoint images. Finally, a feature-enhanced salient object generator is developed to integrate these complementary saliency features, leading to the final salient object predictions for light fields. Quantitative and qualitative experiments demonstrate the superiority of our proposed model against the state-of-the-art methods over the public light field salient object detection datasets.

Temporal data clustering can provide underpinning techniques for the discovery of intrinsic structures, which proved important in condensing or summarizing information demanded in various fields of information sciences, ranging from time series analysis to sequential data understanding. In this paper, we propose a novel hidden Markov model (HMM)-based hybrid meta-clustering ensemble with bi-weighting scheme to solve the problems of initialization and model selection associated with temporal data clustering. To improve the performance of the ensemble techniques, the proposed bi-weighting scheme adaptively examines the partition process and hence optimizes the fusion of consensus functions. Specifically, three consensus functions are used to combine the input partitions, generated by HMM-based K-models under different initializations, into a robust consensus partition. An optimal consensus partition is then selected from the three candidates by a normalized mutual information-based objective function. Finally, the optimal consensus partition is further refined by the HMM-based agglomerative clustering algorithm in association with dendrogram-based similarity partitioning algorithm, leading to the advantage that the number of clusters can be automatically and adaptively determined. Extensive experiments on synthetic data, time series, and real-world motion trajectory datasets illustrate that our proposed approach outperforms all the selected benchmarks and hence providing promising potentials for developing improved clustering tools for information analysis and management.

Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement (LLIE). However, the hand-crafted priors and conventional optimization algorithm adopted to solve the layer decomposition problem result in the lack of adaptivity and efficiency. To this end, this paper proposes a Retinex-based deep unfolding network (URetinex-Net++), which unfolds an optimization problem into a learnable network to decompose a low-light image into reflectance and illumination layers. By formulating the decomposition problem as an implicit priors regularized model, three learning-based modules are carefully designed, responsible for data-dependent initialization, high-efficient unfolding optimization, and fairly-flexible component adjustment, respectively. Particularly, the proposed unfolding optimization module, introducing two networks to adaptively fit implicit priors in the data-driven manner, can realize noise suppression and details preservation for decomposed components. URetinex-Net++ is a further augmented version of URetinex-Net, which introduces a cross-stage fusion block to alleviate the color defect in URetinex-Net. Therefore, boosted performance on LLIE can be obtained in both visual quality and quantitative metrics, where only a few parameters are introduced and little time is cost. Extensive experiments on real-world low-light images qualitatively and quantitatively demonstrate the effectiveness and superiority of the proposed URetinex-Net++ over state-of-the-art methods.