Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

FlowBoost - Appearance Learning from Sparsely Annotated Video

We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists in iteratively improving an appearance based model built with a Boosting procedure, and the reconstruction of trajectories corresponding to the motion of multiple targets. We demonstrate the efficiency of our procedure on pedestrian detection in videos and cell detection in microscopy image sequences. In both cases, our method is demonstrated to reduce the labeling requirement by one to two orders of magnitude. We show that in some instances, our method trained with sparse labels on a video sequence is able to outperform a standard learning procedure trained with the fully labeled sequence

Face Recognition from Weakly Labeled Data

Recognizing the identity of a face or a person in the media usually requires lots of training data to design robust classifiers, which demands a great amount of human effort for annotation. Alternatively, the weakly labeled data is publicly available, but the labels can be ambiguous or noisy. For instance, names in the caption of a news photo provide possible candidates for faces appearing in the image. Names in the screenplays are only weakly associated with faces in the videos. Since weakly labeled data is not explicitly labeled by humans, robust learning methods that use weakly labeled data should suppress the impact of noisy instances or automatically resolve the ambiguities in noisy labels. We propose a method for character identification in a TV-series. The proposed method uses automatically extracted labels by associating the faces with names in the transcripts. Such weakly labeled data often has erroneous labels resulting from errors in detecting a face and synchronization. Our approach achieves robustness to noisy labeling by utilizing several features. We construct track nodes from face and person tracks and utilize information from facial and clothing appearances. We discover the video structure for effective inference by constructing a minimum-distance spanning tree (MST) from the track nodes. Hence, track nodes of similar appearance become adjacent to each other and are likely to have the same identity. The non-local cost aggregation step thus serves as a noise suppression step to reliably recognize the identity of the characters in the video. Another type of weakly labeled data results from labeling ambiguities. In other words, a training sample can have more than one label, and typically one of the labels is the true label. For instance, a news photo is usually accompanied by the captions, and the names provided in the captions can be used as the candidate labels for the faces appearing in the photo. Learning an effective subject classifier from the ambiguously labeled data is called ambiguously labeled learning. We propose a matrix completion framework for predicting the actual labels from the ambiguously labeled instances, and a standard supervised classifier that subsequently learns from the disambiguated labels to classify new data. We generalize this matrix completion framework to handle the issue of labeling imbalance that avoids domination by dominant labels. Besides, an iterative candidate elimination step is integrated with the proposed approach to improve the ambiguity resolution. Recently, video-based face recognition techniques have received significant attention since faces in a video provide diverse exemplars for constructing a robust representation of the target (i.e., subject of interest). Nevertheless, the target face in the video is usually annotated with minimum human effort (i.e., a single bounding box in a video frame). Although face tracking techniques can be utilized to associate faces in a single video shot, it is ineffective for associating faces across multiple video shots. To fully utilize faces of a target in multiples-shot videos, we propose a target face association (TFA) method to obtain a set of images of the target face, and these associated images are then utilized to construct a robust representation of the target for improving the performance of video-based face recognition task. One of the most important applications of video-based face recognition is outdoor video surveillance using a camera network. Face recognition in outdoor environment is a challenging task due to illumination changes, pose variations, and occlusions. We present the taxonomy of camera networks and discuss several techniques for continuous tracking of faces acquired by an outdoor camera network as well as a face matching algorithm. Finally, we demonstrate the real-time video surveillance system using pan-tilt-zoom (PTZ) cameras to perform pedestrian tracking, localization, face detection, and face recognition

DISCRIMINATIVE LEARNING AND RECOGNITION USING DICTIONARIES

In recent years, the theory of sparse representation has emerged as a powerful tool for efficient processing of data in non-traditional ways. This is mainly due to the fact that most signals and images of interest tend to be sparse or compressible in some dictionary. In other words, they can be well approximated by a linear combination of a few elements (also known as atoms) of a dictionary. This dictionary can either be an analytic dictionary composed of wavelets or Fourier basis or it can be directly trained from data. It has been observed that dictionaries learned directly from data provide better representation and hence can improve the performance of many practical applications such as restoration and classification. In this dissertation, we study dictionary learning and recognition under supervised, unsupervised, and semi-supervised settings. In the supervised case, we propose an approach to recognize humans in unconstrained videos, where the main challenge is exploiting the identity information in multiple frames and the accompanying dynamic signature. These identity cues include face, body, and motion. Our approach is based on video-dictionaries for face and body. We design video-dictionaries to implicitly encode temporal, pose, and illumination information. Next, we propose a novel multivariate sparse representation method that jointly represents all the video data by a sparse linear combination of training data. To increase the ability of our algorithm to learn nonlinearities, we apply kernel methods to learn the dictionaries. Next, we address the problem of matching faces across changes in pose in unconstrained videos. Our approach consists of two methods based on 3D rotation and sparse representation that compensate for changes in pose. We demonstrate the superior performance of our approach over several state-of-the-art algorithms through extensive experiments on unconstrained video datasets. In the unsupervised case, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain. The main feature of the proposed approach is that it provides in-plane rotation and scale invariant clustering, which is useful in many applications such as Content Based Image Retrieval (CBIR). We demonstrate through experiments that the proposed rotation and scale invariant clustering provides not only good retrieval performances but also substantial improvements and robustness compared to traditional Gabor-based and several state-of-the-art shape-based methods. We then extend the dictionary learning problem to a generalized semi-supervised formulation, where each training sample is provided with a set of possible labels and only one label among them is the true one. Such applications can be found in image and video collections where one often has only partially labeled data. For instance, given an image with multiple faces and a caption specifying the names, we can be sure that each of the faces belong to one of the names specified, while the exact identity of each face is not known. Labeling involves significant amount of human effort and is expensive. This has motivated researchers to develop learning algorithms from partially labeled training data. In this work, we develop dictionary learning algorithms that utilize such partially labeled data. The proposed method aims to solve the problem of ambiguously labeled multiclass-classification using an iterative algorithm. The dictionaries are updated using either soft (EM-based) or hard decision rules. Extensive evaluations on existing datasets demonstrate that the proposed method performs significantly better than state-of-the-art approaches for learning from ambiguously labeled data. As sparsity plays a major role in our research, we further present a sparse representation-based approach to find the salient views of 3D objects. The salient views are categorized into two groups. The first are boundary representative views that have several visible sides and object surfaces that may be attractive to humans. The second are side representative views that best represent side views of the approximating convex shape. The side representative views are class-specific views and possess the most representative power compared to other within-class views. Using the concept of characteristic view class, we first present a sparse representation-based approach for estimating the boundary representative views. With the estimated boundaries, we determine the side representative views based on a minimum reconstruction error criterion. Furthermore, to evaluate our method, we introduce the notion of geometric dictionaries built from salient views for applications in 3D object recognition, retrieval and sparse-to-full reconstruction. By a series of experiments on four publicly available 3D object datasets, we demonstrate the effectiveness of our approach over state-of-the-art algorithms and baseline methods

Semantic Spaces for Video Analysis of Behaviour

PhDThere are ever growing interests from the computer vision community into human behaviour analysis based on visual sensors. These interests generally include: (1) behaviour recognition - given a video clip or specific spatio-temporal volume of interest discriminate it into one or more of a set of pre-defined categories; (2) behaviour retrieval - given a video or textual description as query, search for video clips with related behaviour; (3) behaviour summarisation - given a number of video clips, summarise out representative and distinct behaviours. Although countless efforts have been dedicated into problems mentioned above, few works have attempted to analyse human behaviours in a semantic space. In this thesis, we define semantic spaces as a collection of high-dimensional Euclidean space in which semantic meaningful events, e.g. individual word, phrase and visual event, can be represented as vectors or distributions which are referred to as semantic representations. With the semantic space, semantic texts, visual events can be quantitatively compared by inner product, distance and divergence. The introduction of semantic spaces can bring lots of benefits for visual analysis. For example, discovering semantic representations for visual data can facilitate semantic meaningful video summarisation, retrieval and anomaly detection. Semantic space can also seamlessly bridge categories and datasets which are conventionally treated independent. This has encouraged the sharing of data and knowledge across categories and even datasets to improve recognition performance and reduce labelling effort. Moreover, semantic space has the ability to generalise learned model beyond known classes which is usually referred to as zero-shot learning. Nevertheless, discovering such a semantic space is non-trivial due to (1) semantic space is hard to define manually. Humans always have a good sense of specifying the semantic relatedness between visual and textual instances. But a measurable and finite semantic space can be difficult to construct with limited manual supervision. As a result, constructing semantic space from data is adopted to learn in an unsupervised manner; (2) It is hard to build a universal semantic space, i.e. this space is always contextual dependent. So it is important to build semantic space upon selected data such that it is always meaningful within the context. Even with a well constructed semantic space, challenges are still present including; (3) how to represent visual instances in the semantic space; and (4) how to mitigate the misalignment of visual feature and semantic spaces across categories and even datasets when knowledge/data are generalised. This thesis tackles the above challenges by exploiting data from different sources and building contextual semantic space with which data and knowledge can be transferred and shared to facilitate the general video behaviour analysis. To demonstrate the efficacy of semantic space for behaviour analysis, we focus on studying real world problems including surveillance behaviour analysis, zero-shot human action recognition and zero-shot crowd behaviour recognition with techniques specifically tailored for the nature of each problem. Firstly, for video surveillances scenes, we propose to discover semantic representations from the visual data in an unsupervised manner. This is due to the largely availability of unlabelled visual data in surveillance systems. By representing visual instances in the semantic space, data and annotations can be generalised to new events and even new surveillance scenes. Specifically, to detect abnormal events this thesis studies a geometrical alignment between semantic representation of events across scenes. Semantic actions can be thus transferred to new scenes and abnormal events can be detected in an unsupervised way. To model multiple surveillance scenes simultaneously, we show how to learn a shared semantic representation across a group of semantic related scenes through a multi-layer clustering of scenes. With multi-scene modelling we show how to improve surveillance tasks including scene activity profiling/understanding, crossscene query-by-example, behaviour classification, and video summarisation. Secondly, to avoid extremely costly and ambiguous video annotating, we investigate how to generalise recognition models learned from known categories to novel ones, which is often termed as zero-shot learning. To exploit the limited human supervision, e.g. category names, we construct the semantic space via a word-vector representation trained on large textual corpus in an unsupervised manner. Representation of visual instance in semantic space is obtained by learning a visual-to-semantic mapping. We notice that blindly applying the mapping learned from known categories to novel categories can cause bias and deteriorating the performance which is termed as domain shift. To solve this problem we employed techniques including semisupervised learning, self-training, hubness correction, multi-task learning and domain adaptation. All these methods in combine achieve state-of-the-art performance in zero-shot human action task. In the last, we study the possibility to re-use known and manually labelled semantic crowd attributes to recognise rare and unknown crowd behaviours. This task is termed as zero-shot crowd behaviours recognition. Crucially we point out that given the multi-labelled nature of semantic crowd attributes, zero-shot recognition can be improved by exploiting the co-occurrence between attributes. To summarise, this thesis studies methods for analysing video behaviours and demonstrates that exploring semantic spaces for video analysis is advantageous and more importantly enables multi-scene analysis and zero-shot learning beyond conventional learning strategies

Face recognition in video surveillance from a single reference sample through domain adaptation

Face recognition (FR) has received significant attention during the past decades in many applications, such as law enforcement, forensics, access controls, information security and video surveillance (VS), due to its covert and non-intrusive nature. FR systems specialized for VS seek to accurately detect the presence of target individuals of interest over a distributed network of video cameras under uncontrolled capture conditions. Therefore, recognizing faces of target individuals in such environment is a challenging problem because the appearance of faces varies due to changes in pose, scale, illumination, occlusion, blur, etc. The computational complexity is also an important consideration because of the growing number of cameras, and the processing time of state-of-the-art face detection, tracking and matching algorithms. In this thesis, adaptive systems are proposed for accurate still-to-video FR, where a single (or very few) reference still or a mug-shot is available to design a facial model for the target individual. This is a common situation in real-world watch-list screening applications due to the cost and feasibility of capturing reference stills, and managing facial models over time. The limited number of reference stills can adversely affect the robustness of facial models to intra-class variations, and therefore the performance of still-to-video FR systems. Moreover, a specific challenge in still-to-video FR is the shift between the enrollment domain, where high-quality reference faces are captured under controlled conditions from still cameras, and the operational domain, where faces are captured with video cameras under uncontrolled conditions. To overcome the challenges of such single sample per person (SSPP) problems, 3 new systems are proposed for accurate still-to-video FR that are based on multiple face representations and domain adaptation. In particular, this thesis presents 3 contributions. These contributions are described with more details in the following statements. In Chapter 3, a multi-classifier framework is proposed for robust still-to-video FR based on multiple and diverse face representations of a single reference face still. During enrollment of a target individual, the single reference face still is modeled using an ensemble of SVM classifiers based on different patches and face descriptors. Multiple feature extraction techniques are applied to patches isolated in the reference still to generate a diverse SVM pool that provides robustness to common nuisance factors (e.g., variations in illumination and pose). The estimation of discriminant feature subsets, classifier parameters, decision thresholds, and ensemble fusion functions is achieved using the high-quality reference still and a large number of faces captured in lower quality video of non-target individuals in the scene. During operations, the most competent subset of SVMs are dynamically selected according to capture conditions. Finally, a head-face tracker gradually regroups faces captured from different people appearing in a scene, while each individual-specific ensemble performs face matching. The accumulation of matching scores per face track leads to a robust spatio-temporal FR when accumulated ensemble scores surpass a detection threshold. Experimental results obtained with the Chokepoint and COX-S2V datasets show a significant improvement in performance w.r.t. reference systems, especially when individual-specific ensembles (1) are designed using exemplar-SVMs rather than one-class SVMs, and (2) exploit score-level fusion of local SVMs (trained using features extracted from each patch), rather than using either decision-level or feature-level fusion with a global SVM (trained by concatenating features extracted from patches). In Chapter 4, an efficient multi-classifier system (MCS) is proposed for accurate still-to-video FR based on multiple face representations and domain adaptation (DA). An individual-specific ensemble of exemplar-SVM (e-SVM) classifiers is thereby designed to improve robustness to intra-class variations. During enrollment of a target individual, an ensemble is used to model the single reference still, where multiple face descriptors and random feature subspaces allow to generate a diverse pool of patch-wise classifiers. To adapt these ensembles to the operational domains, e-SVMs are trained using labeled face patches extracted from the reference still versus patches extracted from cohort and other non-target stills mixed with unlabeled patches extracted from the corresponding face trajectories captured with surveillance cameras. During operations, the most competent classifiers per given probe face are dynamically selected and weighted based on the internal criteria determined in the feature space of e-SVMs. This chapter also investigates the impact of using different training schemes for DA, as well as, the validation set of non-target faces extracted from stills and video trajectories of unknown individuals in the operational domain. The results indicate that the proposed system can surpass state-of-the-art accuracy, yet with a significantly lower computational complexity. In Chapter 5, a deep convolutional neural network (CNN) is proposed to cope with the discrepancies between facial regions of interest (ROIs) isolated in still and video faces for robust still-to-video FR. To that end, a face-flow autoencoder CNN called FFA-CNN is trained using both still and video ROIs in a supervised end-to-end multi-task learning. A novel loss function containing a weighted combination of pixel-wise, symmetry-wise and identity preserving losses is introduced to optimize the network parameters. The proposed FFA-CNN incorporates a reconstruction network and a fully-connected classification network, where the former reconstructs a well-illuminated frontal ROI with neutral expression from a pair of low-quality non-frontal video ROIs and the latter is utilized to compare the still and video representations to provide matching scores. Thus, integrating the proposed weighted loss function with a supervised end-to-end training approach leads to generate high-quality frontal faces and learn discriminative face representations similar for the same identities. Simulation results obtained over challenging COX Face DB confirm the effectiveness of the proposed FFA-CNN to achieve convincing performance compared to current state-of-the-art CNN-based FR systems

Improving Visual Recognition With Unlabeled Data

The success of deep neural networks has resulted in computer vision systems that obtain high accuracy on a wide variety of tasks such as image classification, object detection, semantic segmentation, etc. However, most state-of-the-art vision systems are dependent upon large amounts of labeled training data, which is not a scalable solution in the long run. This work focuses on improving existing models for visual object recognition and detection without being dependent on such large-scale human-annotated data. We first show how large numbers of hard examples (cases where an existing model makes a mistake) can be obtained automatically from unlabeled video sequences by exploiting temporal consistency cues in the output of a pre-trained object detector. These examples can strongly influence a model\u27s parameters when the network is re-trained to correct them, resulting in improved performance on several object detection tasks. Further, such hard examples from unlabeled videos can be used to address the problem of unsupervised domain adaptation. We focus on the automatic adaptation of an existing object detector to a new domain with no labeled data, assuming that a large number of unlabeled videos are readily available. Our approach is evaluated on challenging face and pedestrian detection tasks involving large domain shifts, showing improved performance with minimal dependence on hyper-parameters. Finally, we address the problem of face recognition, which has achieved high accuracy by employing deep neural networks trained on massive labeled datasets. Further improvements through supervised learning require significantly larger datasets and hence massive annotation efforts. We improve upon the performance of face recognition models trained on large-scale labeled datasets by using unlabeled faces as additional training data. We present insights and recipes for training deep face recognition models with labeled and unlabeled data at scale, addressing real-world challenges such as overlapping identities between the labeled and unlabeled datasets, as well as label noise introduced by clustering errors