An attention model and its application in man-made scene interpretation

The ultimate aim of research into computer vision is designing a system which interprets its surrounding environment in a similar way the human can do effortlessly. However, the state of technology is far from achieving such a goal. In this thesis different components of a computer vision system that are designed for the task of interpreting man-made scenes, in particular images of buildings, are described. The flow of information in the proposed system is bottom-up i.e., the image is first segmented into its meaningful components and subsequently the regions are labelled using a contextual classifier. Starting from simple observations concerning the human vision system and the gestalt laws of human perception, like the law of “good (simple) shape” and “perceptual grouping”, a blob detector is developed, that identifies components in a 2D image. These components are convex regions of interest, with interest being defined as significant gradient magnitude content. An eye tracking experiment is conducted, which shows that the regions identified by the blob detector, correlate significantly with the regions which drive the attention of viewers. Having identified these blobs, it is postulated that a blob represents an object, linguistically identified with its own semantic name. In other words, a blob may contain a window a door or a chimney in a building. These regions are used to identify and segment higher order structures in a building, like facade, window array and also environmental regions like sky and ground. Because of inconsistency in the unary features of buildings, a contextual learning algorithm is used to classify the segmented regions. A model which learns spatial and topological relationships between different objects from a set of hand-labelled data, is used. This model utilises this information in a MRF to achieve consistent labellings of new scenes

Vision-based retargeting for endoscopic navigation

Endoscopy is a standard procedure for visualising the human gastrointestinal tract. With the advances in biophotonics, imaging techniques such as narrow band imaging, confocal laser endomicroscopy, and optical coherence tomography can be combined with normal endoscopy for assisting the early diagnosis of diseases, such as cancer. In the past decade, optical biopsy has emerged to be an effective tool for tissue analysis, allowing in vivo and in situ assessment of pathological sites with real-time feature-enhanced microscopic images. However, the non-invasive nature of optical biopsy leads to an intra-examination retargeting problem, which is associated with the difficulty of re-localising a biopsied site consistently throughout the whole examination. In addition to intra-examination retargeting, retargeting of a pathological site is even more challenging across examinations, due to tissue deformation and changing tissue morphologies and appearances. The purpose of this thesis is to address both the intra- and inter-examination retargeting problems associated with optical biopsy. We propose a novel vision-based framework for intra-examination retargeting. The proposed framework is based on combining visual tracking and detection with online learning of the appearance of the biopsied site. Furthermore, a novel cascaded detection approach based on random forests and structured support vector machines is developed to achieve efficient retargeting. To cater for reliable inter-examination retargeting, the solution provided in this thesis is achieved by solving an image retrieval problem, for which an online scene association approach is proposed to summarise an endoscopic video collected in the first examination into distinctive scenes. A hashing-based approach is then used to learn the intrinsic representations of these scenes, such that retargeting can be achieved in subsequent examinations by retrieving the relevant images using the learnt representations. For performance evaluation of the proposed frameworks, extensive phantom, ex vivo and in vivo experiments have been conducted, with results demonstrating the robustness and potential clinical values of the methods proposed.Open Acces

Similarity learning for person re-identification and semantic video retrieval

Many computer vision problems boil down to the learning of a good visual similarity function that calculates a score of how likely two instances share the same semantic concept. In this thesis, we focus on two problems related to similarity learning: Person Re-Identification, and Semantic Video Retrieval. Person Re-Identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. Starting with two cameras, we propose a novel visual word co-occurrence based appearance model to measure the similarities between pedestrian images. This model naturally accounts for spatial similarities and variations caused by pose, illumination and configuration changes across camera views. As a generalization to multiple camera views, we introduce the Group Membership Prediction (GMP) problem. The GMP problem involves predicting whether a collection of instances shares the same semantic property. In this context, we propose a novel probability model and introduce latent view-specific and view-shared random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our method is tested on various benchmarks demonstrating superior accuracy over state-of-art. Semantic Video Retrieval seeks to match complex activities in a surveillance video to user described queries. In surveillance scenarios with noise and clutter usually present, visual uncertainties introduced by error-prone low-level detectors, classifiers and trackers compose a significant part of the semantic gap between user defined queries and the archive video. To bridge the gap, we propose a novel probabilistic activity localization formulation that incorporates learning of object attributes, between-object relationships, and object re-identification without activity-level training data. Our experiments demonstrate that the introduction of similarity learning components effectively compensate for noise and error in previous stages, and result in preferable performance on both aerial and ground surveillance videos. Considering the computational complexity of our similarity learning models, we attempt to develop a way of training complicated models efficiently while remaining good performance. As a proof-of-concept, we propose training deep neural networks for supervised learning of hash codes. With slight changes in the optimization formulation, we could explore the possibilities of incorporating the training framework for Person Re-Identification and related problems.2019-07-09T00:00:00

Real-time Surveillance Application by Multiple Detectors and Compressive Trackers

A real-time surveillance system for IP network cameras is presented. Motion, part-body, and whole-body detectors are efficiently combined to generate robust and fast detections, which feed multiple compressive trackers. The generated trajectories are then improved using a reidentification strategy for long term operation

Visual Data Association: Tracking, Re-identification and Retrieval

As there is a rapid development of the information society, large amounts of multimedia data are generated, which are shared and transferred on various electronic devices and the Internet every minute. Hence, building intelligent systems capable of associating these visual data at diverse locations and different times is absolutely essential and will significantly facilitate understanding and identifying where an object came from and where it is going. Thus, the estimated traces of motions or changes increasingly make it feasible to implement advanced algorithms to real-world applications, including human-computer interaction, robotic navigation, security in surveillance, biological characteristics association and civil structure vibration detection. However, due to the inherent challenges, such as ambiguity, heterogeneity, noisy data, large-scale property and unknown variations, visual data association is currently far from being established. Therefore, this thesis focuses on the studies of associating visual data at diverse locations and different times for the tasks of tracking, re-identification and retrieval. More specifically, three situations including single camera, across multiple cameras and across multiple modalities have been investigated and four algorithms have been developed at different levels. Chapter 3 The first algorithm is to explore an ensemble system for robust object tracking, primarily considering the independence of classifier members. An empirical analysis is firstly given to show that object tracking is a non-i.i.d. sampling, under-sample and incomplete-dataset problem. Then, a set of independent classifiers trained sequentially on different small datasets is dynamically maintained to overcome the particular machine learning problem. Thus, for every challenge, an optimal classifier can be approximated in a subspace spanned by the selected competitive classifiers. Chapter 4 The second method is to improve the object tracking by exploiting a winner-take-all strategy to select the most suitable trackers. This topic naturally extends the concept of ensemble in the first topic to a more general idea: a multi-expert system, in which members come from different function spaces. Thus, the diversity of the system is more likely to be amplified. Based on a large public dataset, a prediction model of performance for different trackers on various challenges can be obtained off-line. Then, the learned structural regression model can be directly used to efficiently select the winner tracker online. Chapter 5 The third one is to learn cross-view identities for fast person re-identification, in a cross-camera setting, which significantly differs from the single-view object tracking in the first two topics. Two sets of discriminative hash functions for two different views are learned by simultaneously minimising their distance in the Hamming space, and maximising the cross-covariance and margin. Thus, similar binary codes can be found for images of the same person captured at different views by embedding the images into the Hamming space. Chapter 6 The fourth model is to develop a novel Hetero-manifold regularisation framework for efficient cross-modal retrieval. Compared with the first two settings, this is a more general and complex topic, in which the samples can be relaxed to the images captured in the very far distance or very long time, even to text, voice and other formats. Taking advantage of the hetero-manifold, the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero-manifold. It is concluded that, by fully exploiting the algorithms for solving the problems in the three situations, an integrated trace for an object moving anywhere can be definitely discovered

Towards Privacy and Security Concerns of Adversarial Examples in Deep Hashing Image Retrieval

With the explosive growth of images on the internet, image retrieval based on deep hashing attracts spotlights from both research and industry communities. Empowered by deep neural networks (DNNs), deep hashing enables fast and accurate image retrieval on large-scale data. However, inheriting from deep learning, deep hashing remains vulnerable to specifically designed input, called adversarial examples. By adding imperceptible perturbations on inputs, adversarial examples fool DNNs to make wrong decisions. The existence of adversarial examples not only raises security concerns for real-world deep learning applications, but also provides us with a technique to confront malicious applications. In this dissertation, we investigate privacy and security concerns in deep hashing image retrieval systems related to adversarial examples. Starting with a privacy concern, we stand on users side to preserve privacy information in images, which can be extracted by adversaries by retrieving similar images in image retrieval systems. Existing image processing-based privacy-preserving methods suffer from a trade-off of efficacy and usability. We propose a method introducing imperceptible adversarial perturbations on original images to prevent them from being retrieved. Users upload protected adversarial images instead of the original images to preserve privacy while maintaining usability. Then we shift to the security concerns. We act as attackers, proactively providing adversarial images to retrieval systems. These adversarial examples are embedded to specific targets so that the user retrieval results contain our unrelated adversarial images, e.g., users query with a “Husky dog” image, but retrieve adversarial “dog food” images in the result. A transferability-based attack is proposed for black-box models. We improve black-box transferability with the random noise as the proxy in optimization, achieving state-of-the-art success rate. Finally, we stand on retrieval systems side to mitigate the security concerns of adversarial attacks in deep hashing image retrieval. We propose a detection method that detects adversarial examples in the inference time. By studying unique adversarial behaviors in deep hashing image retrieval, our proposed method is constructed on criterions of these adversarial behaviors. The proposed method detects most of the adversarial examples with minimum overhead

Active Collaboration of Classifiers for Visual Tracking

Recently, discriminative visual trackers obtain state-of-the-art performance, yet they suffer in the presence of different real-world challenges such as target motion and appearance changes. In a discriminative tracker, one or more classifiers are employed to obtain the target/nontarget label for the samples, which in turn determine the target’s location. To cope with variations of the target shape and appearance, the classifier(s) are updated online with different samples of the target and the background. Sample selection, labeling, and updating the classifier are prone to various sources of errors that drift the tracker. In this study, we motivate, conceptualize, realize, and formalize a novel active co-tracking framework, step by step to demonstrate the challenges and generic solutions for them. In this framework, not only classifiers cooperate in labeling the samples but also exchange their information to robustify the labeling, improve the sampling, and realize efficient yet effective updating. The proposed framework is evaluated against state-of-the-art trackers on public dataset and showed promising results

Similarity learning for person re-identification and semantic video retrieval

Many computer vision problems boil down to the learning of a good visual similarity function that calculates a score of how likely two instances share the same semantic concept. In this thesis, we focus on two problems related to similarity learning: Person Re-Identification, and Semantic Video Retrieval. Person Re-Identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. Starting with two cameras, we propose a novel visual word co-occurrence based appearance model to measure the similarities between pedestrian images. This model naturally accounts for spatial similarities and variations caused by pose, illumination and configuration changes across camera views. As a generalization to multiple camera views, we introduce the Group Membership Prediction (GMP) problem. The GMP problem involves predicting whether a collection of instances shares the same semantic property. In this context, we propose a novel probability model and introduce latent view-specific and view-shared random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our method is tested on various benchmarks demonstrating superior accuracy over state-of-art. Semantic Video Retrieval seeks to match complex activities in a surveillance video to user described queries. In surveillance scenarios with noise and clutter usually present, visual uncertainties introduced by error-prone low-level detectors, classifiers and trackers compose a significant part of the semantic gap between user defined queries and the archive video. To bridge the gap, we propose a novel probabilistic activity localization formulation that incorporates learning of object attributes, between-object relationships, and object re-identification without activity-level training data. Our experiments demonstrate that the introduction of similarity learning components effectively compensate for noise and error in previous stages, and result in preferable performance on both aerial and ground surveillance videos. Considering the computational complexity of our similarity learning models, we attempt to develop a way of training complicated models efficiently while remaining good performance. As a proof-of-concept, we propose training deep neural networks for supervised learning of hash codes. With slight changes in the optimization formulation, we could explore the possibilities of incorporating the training framework for Person Re-Identification and related problems.2019-07-09T00:00:00