Efficient management techniques for large video collections

In this research, we focus on two techniques related to the management of large video collection: video copy detection and automatic video classification. After the introductory chapter and a brief review in Chapter 2, our main research results are presented in Chapters 3 and 4.In Chapter 3, a fast duplicate video detection system based on the camera transitional behavior and the suffix array data structure is proposed. The proposed system matches video clips according to their temporal structures, which are represented by a set of frames corresponding to unique events, called anchor frames. Noticing the natural association between the camera operation and the resulting video, we use the camera transitional behavior to indicate the unique events. Specifically, shot boundaries and the begin and end points of camera panning and tilting movements are detected as anchor frames. The length between adjacent anchor frames is computed to form a one-dimensional sequence, called the gap sequence, which serves as the signature of the video. An efficient gap sequence matching algorithm based on the suffix array data structure is adopted to match two given video signatures, which can achieve linear-time processing. A candidate pruning stage is also proposed to reduce the computation as much as possible. Specifically, video clips that are very unlikely to be duplicates of the input query video are eliminated in this stage before the signature matching is performed. Experimental results show that the proposed framework is not only efficient (in terms of computational speed) but also effective (in terms of high accuracy) in identifying duplicate video pairs.In Chapter 4, two novel features that take the shooting process into consideration are first proposed for video genre classification, which are the number of camera used in a short time interval, and distance of the camera to the shooting subject. Preliminary experiment results show that the proposed features capture additional genre-related information. Some conclusion about the genre can be inferred from the proposed features to a certain degree. Then the properties of amateur and professional video clips are observed and analyzed. Although a large amount of work has been proposed by considering cinematic principles, most extracted features are low-level features without much semantic information. In the proposed scheme, features are designed to take the camera operation and the nature of amateur video clips into account. These features address various differences in video quality and editing effects. They are tested on video clips collected from an Internet video sharing website, with several classifiers. Experimental results on this test video data set demonstrate that the camera usage can be inferred from the proposed features and, thus, reliable separation of professional and amateur video contents can be achieved.Concluding remarks and future extensions are given in Chapter 5.