Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Exploring semantic concepts for complex event analysis in unconstrained video clips

University of Technology Sydney. Faculty of Engineering and Information Technology.Modern consumer electronics (e.g. smart phones) have made video acquisition convenient for the general public. Consequently, the number of videos freely available on the Internet has been exploding, thanks also to the appearance of large video hosting websites (e.g. Youtube). Recognizing complex events from these unconstrained videos has been receiving increasing interest in the multimedia and computer vision field. Compared with visual concepts such as actions, scenes and objects, event detection is more challenging in the following aspects. Firstly, an event is a higher level semantic abstraction of video sequences than a concept and consists of multiple concepts. Secondly, a concept can be detected in a shorter video sequence or even in a single frame but an event is usually contained in a longer video clip. Thirdly, different video sequences of a particular event may have dramatic variations. The most commonly used technique for complex event detection is to aggregate low-level visual features and then feed them to sophisticated statistical classification machines. However, these methodologies fail to provide any interpretation of the abundant semantic information contained in a complex video event, which impedes efficient high-level event analysis, especially when the training exemplars are scarce in real-world applications. A recent trend in this direction is to employ some high-level semantic representation, which can be advantageous for subsequent event analysis tasks. These approaches lead to improved generalization capability and allow zero-shot learning (i.e. recognizing new events that are never seen in the training phase). In addition, they provide a meaningful way to aggregate low-level features, and yield more interpretable results, hence may facilitate other video analysis tasks such as retrieval on top of many low-level features, and have roots in object and action recognition. Although some promising results have been achieved, current event analysis systems still have some inherent limitations. 1) They fail to consider the fact that only a few shots in a long video are relevant to the event of interest while others are irrelevant or even misleading. 2) They are not capable of leveraging the mutual benefits of Multimedia Event Detection (MED) and Multimedia Event Recounting (MER), especially when the number of training exemplars is small. 3) They did not consider the differences of the classifier’s prediction capability on individual testing videos. 4) The unreliability of the semantic concept detectors, due to lack of labeled training videos, has been largely unaddressed. To solve these challenges, in this thesis, we aim to develop a series of statistical learning methods to explore semantic concepts for complex event analysis in unconstrained video clips. Our works are summarized as follows: In Chapter 2, we propose a novel semantic pooling approach for challenging tasks on long untrimmed Internet videos, especially when only a few shots/segments are relevant to the event of interest while many other shots are irrelevant or event misleading. The commonly adopted pooling strategies aggregate the shots indifferently in one way or another, resulting in a great loss of information. Instead, we first define a novel notion of semantic saliency that assess the relevance of each shot with the event of interest. We then prioritize the shots according to their saliency scores since shots that are semantically more salient are expected to contribute more to the final event analysis. Next, we propose a new isotonic regularizer that is able to exploit the constructed semantic ordering information. The resulting nearly-isotonic SVM classifier exhibits higher discriminative power in event detection and recognition tasks. Computationally, we develop an efficient implementation using the proximal gradient algorithm, and we prove new and closed-form proximal steps. In Chapter 3, we develop a joint event detection and evidence recounting framework with limited supervision, which is able to leverage the mutual benefits of MED and MER. Different from most existing systems that perform MER as a post-processing step on top of the MED results, the proposed framework simultaneously detects high-level events and localizes the indicative concepts of the events. Our premise is that a good recounting algorithm should not only explain the detection result, but should also be able to assist detection in the first place. Coupled in a joint optimization framework, recounting improves detection by pruning irrelevant noisy concepts while detection directs recounting to the most discriminative evidences. To better utilize the powerful and interpretable semantic video representation, we segment each video into several shots and exploit the rich temporal structures at shot level. The consequent computational challenge is carefully addressed through a significant improvement of the current ADMM algorithm, which, after eliminating all inner loops and equipping novel closed-form solutions for all intermediate steps, enables us to efficiently process extremely large video corpora. In Chapter 4, we propose an Event-Driven Concept Weighting framework to automatically detect events without the use of visual training exemplars. In principle, zero-shot learning makes it possible to train an event detection model based on the assumption that events (e.g. birthday party) can be described by multiple mid-level semantic concepts (e.g. “blowing candle”, “birthday cake”). Towards this goal, we first pre-train a bundle of concept classifiers using data from other sources, which are applied on all test videos to obtain multiple prediction score vectors. Existing methods generally combine the predictions of the concept classifiers with fixed weights, and ignore the fact that each concept classifier may perform better or worse for different subset of videos. To address this issue, we propose to learn the optimal weights of the concept classifiers for each testing video by exploring a set of online available videos which have free-form text descriptions of their content. To be specific, our method is built upon the local smoothness property, which assumes that visually similar videos have comparable labels within a local region of the same space. In Chapter 5, we develop a novel approach to estimate the reliability of the concept classifiers without labeled training videos. The EDCW framework proposed in Chapter 4, as well as most existing works on semantic event search, ignore the fact that not all concept classifiers are equally reliable, especially when they are trained from other source domains. For example, “face” in video frames can now be reasonably accurately detected, but in contrast, the action “brush teeth” remains hard to recognize in short video clips. Consequently, a relevant concept can be of limited use or even misuse if its classifier is highly unreliable. Therefore, when combining concept scores, we propose to take their relevance, predictive power, and reliability all into account. This is achieved through a novel extension of the spectral meta-learner, which provided a principled way to estimate classifier accuracies using purely unlabeled data

Beat-Event Detection in Action Movie Franchises

While important advances were recently made towards temporally localizing and recognizing specific human actions or activities in videos, efficient detection and classification of long video chunks belonging to semantically defined categories such as "pursuit" or "romance" remains challenging.We introduce a new dataset, Action Movie Franchises, consisting of a collection of Hollywood action movie franchises. We define 11 non-exclusive semantic categories - called beat-categories - that are broad enough to cover most of the movie footage. The corresponding beat-events are annotated as groups of video shots, possibly overlapping.We propose an approach for localizing beat-events based on classifying shots into beat-categories and learning the temporal constraints between shots. We show that temporal constraints significantly improve the classification performance. We set up an evaluation protocol for beat-event localization as well as for shot classification, depending on whether movies from the same franchise are present or not in the training data

Adaptive Information Cluster at Dublin City University

The Adaptive Information Cluster (AIC) is a collaboration between Dublin City University and University College Dublin, and in the AIC at DCU, we investigate and develop as one stream of our research activities, various content analysis tools that can automatically index and structure video information. This includes movies or CCTV footage and the motivation is to support useful searching and browsing features for the envisaged end-users of such systems. We bring in the HCI perspective to this highly-technically-oriented research by brainstorming, generating scenarios, sketching and prototyping the user-interfaces to the resulting video retrieval systems we develop, and we conduct usability studies to better understand the usage and opinions of such systems so as to guide the future direction of our technological research

Large scale evaluations of multimedia information retrieval: the TRECVid experience

Information Retrieval is a supporting technique which underpins a broad range of content-based applications including retrieval, filtering, summarisation, browsing, classification, clustering, automatic linking, and others. Multimedia information retrieval (MMIR) represents those applications when applied to multimedia information such as image, video, music, etc. In this presentation and extended abstract we are primarily concerned with MMIR as applied to information in digital video format. We begin with a brief overview of large scale evaluations of IR tasks in areas such as text, image and music, just to illustrate that this phenomenon is not just restricted to MMIR on video. The main contribution, however, is a set of pointers and a summarisation of the work done as part of TRECVid, the annual benchmarking exercise for video retrieval tasks

Balancing the power of multimedia information retrieval and usability in designing interactive TV

Steady progress in the field of multimedia information retrieval (MMIR) promises a useful set of tools that could provide new usage scenarios and features to enhance the user experience in today s digital media applications. In the interactive TV domain, the simplicity of interaction is more crucial than in any other digital media domain and ultimately determines the success or otherwise of any new applications. Thus when integrating emerging tools like MMIR into interactive TV, the increase in interface complexity and sophistication resulting from these features can easily reduce its actual usability. In this paper we describe a design strategy we developed as a result of our e®ort in balancing the power of emerging multimedia information retrieval techniques and maintaining the simplicity of the interface in interactive TV. By providing multiple levels of interface sophistication in increasing order as a viewer repeatedly presses the same button on their remote control, we provide a layered interface that can accommodate viewers requiring varying degrees of power and simplicity. A series of screen shots from the system we have actually developed and built illustrates how this is achieved