Data Driven Approaches for Image & Video Understanding: from Traditional to Zero-shot Supervised Learning

In the present age of advanced computer vision, the necessity of (user-annotated) data is a key factor in image & video understanding. Recent success of deep learning on large scale data has only acted as a catalyst. There are certain problems that exist in this regard: 1) scarcity of (annotated) data, 2) need of expensive manual annotation, 3) problem of change in domain, 4) knowledge base not exhaustive. To make efficient learning systems, one has to be prepared to deal with such diverse set of problems. In terms of data availability, extensive manual annotation can be beneficial in obtaining category specific knowledge. Even then, learning efficient representation for the related task is challenging and requires special attention. On the other hand, when labelled data is scarce, learning category specific representation itself becomes challenging. In this work, I investigate data driven approaches that cater to traditional supervised learning setup as well as an extreme case of data scarcity where no data from test classes are available during training, known as zero-shot learning. First, I look into supervised learning setup with ample annotations and propose efficient dictionary learning technique for better learning of data representation for the task of action classification in images & videos. Then I propose robust mid-level feature representations for action videos that are equally effective in traditional supervised learning as well as zero-shot learning. Finally, I come up with novel approach that cater to zero-shot learning specifically. Thorough discussions followed by experimental validations establish the worth of these novel techniques in solving computer vision related tasks under varying data-dependent scenarios

Large-scale interactive exploratory visual search

Large scale visual search has been one of the challenging issues in the era of big data. It demands techniques that are not only highly effective and efficient but also allow users conveniently express their information needs and refine their intents. In this thesis, we focus on developing an exploratory framework for large scale visual search. We also develop a number of enabling techniques in this thesis, including compact visual content representation for scalable search, near duplicate video shot detection, and action based event detection. We propose a novel scheme for extremely low bit rate visual search, which sends compressed visual words consisting of vocabulary tree histogram and descriptor orientations rather than descriptors. Compact representation of video data is achieved through identifying keyframes of a video which can also help users comprehend visual content efficiently. We propose a novel Bag-of-Importance model for static video summarization. Near duplicate detection is one of the key issues for large scale visual search, since there exist a large number nearly identical images and videos. We propose an improved near-duplicate video shot detection approach for more effective shot representation. Event detection has been one of the solutions for bridging the semantic gap in visual search. We particular focus on human action centred event detection. We propose an enhanced sparse coding scheme to model human actions. Our proposed approach is able to significantly reduce computational cost while achieving recognition accuracy highly comparable to the state-of-the-art methods. At last, we propose an integrated solution for addressing the prime challenges raised from large-scale interactive visual search. The proposed system is also one of the first attempts for exploratory visual search. It provides users more robust results to satisfy their exploring experiences

Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin

Structural learning for large scale image classification

To leverage large-scale collaboratively-tagged (loosely-tagged) images for training a large number of classifiers to support large-scale image classification, we need to develop new frameworks to deal with the following issues: (1) spam tags, i.e., tags are not relevant to the semantic of the images; (2) loose object tags, i.e., multiple object tags are loosely given at the image level without their locations in the images; (3) missing object tags, i.e. some object tags are missed due to incomplete tagging; (4) inter-related object classes, i.e., some object classes are visually correlated and their classifiers need to be trained jointly instead of independently; (5) large scale object classes, which requires to limit the computational time complexity for classifier training algorithms as well as the storage spaces for intermediate results. To deal with these issues, we propose a structural learning framework which consists of the following key components: (1) cluster-based junk image filtering to address the issue of spam tags; (2) automatic tag-instance alignment to address the issue of loose object tags; (3) automatic missing object tag prediction; (4) object correlation network for inter-class visual correlation characterization to address the issue of missing tags; (5) large-scale structural learning with object correlation network for enhancing the discrimination power of object classifiers. To obtain enough numbers of labeled training images, our proposed framework leverages the abundant web images and their social tags. To make those web images usable, tag cleansing has to be done to neutralize the noise from user tagging preferences, in particularly junk tags, loose tags and missing tags. Then a discriminative learning algorithm is developed to train a large number of inter-related classifiers for achieving large-scale image classification, e.g., learning a large number of classifiers for categorizing large-scale images into a large number of inter-related object classes and image concepts. A visual concept network is first constructed for organizing enumorus object classes and image concepts according to their inter-concept visual correlations. The visual concept network is further used to: (a) identify inter-related learning tasks for classifier training; (b) determine groups of visually-similar object classes and image concepts; and (c) estimate the learning complexity for classifier training. A large-scale discriminative learning algorithm is developed for supporting multi-class classifier training and achieving accurate inter-group discrimination and effective intra-group separation. Our discriminative learning algorithm can significantly enhance the discrimination power of the classifiers and dramatically reduce the computational cost for large-scale classifier training

Representing 3D shape in sparse range images for urban object classification

This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing

Representing 3D shape in sparse range images for urban object classification

This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing

Automatic annotation for weakly supervised learning of detectors

PhDObject detection in images and action detection in videos are among the most widely studied computer vision problems, with applications in consumer photography, surveillance, and automatic media tagging. Typically, these standard detectors are fully supervised, that is they require a large body of training data where the locations of the objects/actions in images/videos have been manually annotated. With the emergence of digital media, and the rise of high-speed internet, raw images and video are available for little to no cost. However, the manual annotation of object and action locations remains tedious, slow, and expensive. As a result there has been a great interest in training detectors with weak supervision where only the presence or absence of object/action in image/video is needed, not the location. This thesis presents approaches for weakly supervised learning of object/action detectors with a focus on automatically annotating object and action locations in images/videos using only binary weak labels indicating the presence or absence of object/action in images/videos. First, a framework for weakly supervised learning of object detectors in images is presented. In the proposed approach, a variation of multiple instance learning (MIL) technique for automatically annotating object locations in weakly labelled data is presented which, unlike existing approaches, uses inter-class and intra-class cue fusion to obtain the initial annotation. The initial annotation is then used to start an iterative process in which standard object detectors are used to refine the location annotation. Finally, to ensure that the iterative training of detectors do not drift from the object of interest, a scheme for detecting model drift is also presented. Furthermore, unlike most other methods, our weakly supervised approach is evaluated on data without manual pose (object orientation) annotation. Second, an analysis of the initial annotation of objects, using inter-class and intra-class cues, is carried out. From the analysis, a new method based on negative mining (NegMine) is presented for the initial annotation of both object and action data. The NegMine based approach is a much simpler formulation using only inter-class measure and requires no complex combinatorial optimisation but can still meet or outperform existing approaches including the previously pre3 sented inter-intra class cue fusion approach. Furthermore, NegMine can be fused with existing approaches to boost their performance. Finally, the thesis will take a step back and look at the use of generic object detectors as prior knowledge in weakly supervised learning of object detectors. These generic object detectors are typically based on sampling saliency maps that indicate if a pixel belongs to the background or foreground. A new approach to generating saliency maps is presented that, unlike existing approaches, looks beyond the current image of interest and into images similar to the current image. We show that our generic object proposal method can be used by itself to annotate the weakly labelled object data with surprisingly high accuracy