Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.

The prevalent use of surveillance cameras in public places and advancements in computer vision warrant most sought-after research in the domain of anomalous activity detection. Anomaly detection has shown promising applications for suspicious activity detection. In this paper, we propose a bagging framework IBaggedFCNet that leverages the power of ensembles for robust classification to detect anomalies in videos. Our approach, which investigates state-of-the-art Inception-v3 image classification network, requires no video segmentation prior to feature extraction that can produce unstable segmentation results and cause a high memory footprint. We show improvement empirically on multiple benchmark datasets, most prominently on the UCF-Crime dataset. Moreover, we experiment with different ensemble fusion methods, including static and dynamic techniques, and also prove our single model’s predictive accuracy in localizing anomaly in surveillance videos.

In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling approaches assume that the instances in the bag have no relationship among them. This assumption is unsuited, as the instances in the bags are rarely independent in diverse MIL applications. In contrast, the instance relationship assumption-based techniques incorporate the instance relationship information in the classification process. However, in MIL, the bag composition process is complicated, and it may be possible that instances in one bag are related and instances in another bag are not. In present MIL algorithms, this relationship assumption is not explicitly modeled. The learning algorithm is trained based on one of two relationship assumptions (whether instances in all bags have a relationship or not). Hence, it is essential to model the assumption of instance relationships in the bag classification process. This paper proposes a robust approach that generates vector representation for the bag for both assumptions and the representation selection process to determine whether to consider the instances related or unrelated in the bag classification process. This process helps to determine the essential bag representation vector for every individual bag. The proposed method utilizes attention pooling and vision transformer approaches to generate bag representation vectors. Later, the representation selection subnetwork determines the vector representation essential for bag classification in an end-to-end trainable manner. The generalization abilities of the proposed framework are demonstrated through extensive experiments on several benchmark datasets. The experiments demonstrate that the proposed approach outperforms other state-of-the-art MIL approaches in bag classification.

Vehicle make and model recognition is an important component of the Intelligent Transport System (ITS), which plays an essential role in the vehicle surveillance and traffic monitoring. In this paper, we evaluated the performance of the recent deep neural networks for vehicle recognition to identify 196 different types of vehicles based on their make, model, and year using Stanford Cars data. Transfer learning has been employed to reduce the training time and also added a dropout of 0.5 at the last dense layer before the output layer for improved generalization performance on an applied dataset. Different augmentation methods such as random erasing, horizontal flip, random crop, resize, rotate, shear, and sharpen were applied to the training dataset to increase the number of images. Besides, optimization methods namely mixup and cyclical learning rate were also investigated to train these deep neural networks to avoid overfitting. Finally, the proposed ensemble learning framework using k-fold cross-validation has resulted in the improved accuracy of image classification for the vehicle make and model recognition. The final achieved accuracy is 93.96% through ensembling homogeneous models of DenseNet201, which is higher than previous state-of-the-art approaches for the vehicle make and model recognition. Simultaneously, it has also improved the performance of other models where MobileNetV2 has shown the highest improvement with an increase of 2.8% in the overall accuracy using the proposed ensemble learning technique.