Video segmentation is the first step to most content-based video analysis. In this thesis, several methods have been proposed to detect shot transitions including cut and wipe. In particular, a new cut detection method is proposed to apply multi-adaptive thresholds during three-step processing of frame-by-frame discontinuity values. A "likelihood value", which measures the possibility of the presence of a cut at each step of processing, is used to reduce the influence of threshold selection to the detection performance. A wipe detection algorithm is also proposed in our thesis to detect various wipe effects with accurate frame ranges. In the algorithm, we carefully model a wipe based on its properties and then use the model to remove possible confusion caused by motion or other transition effects. / With the segmented video shots, video indexing and retrieval systems retrieve video shots using shot-based similarity matching based on the features of shot key-frames. Most shot-based similarity matching methods focus on low-level features such as color and texture. Those methods are often not effective enough in video retrieval due to the large gap between semantic interpretation of videos and the low level features. In this thesis, we propose an attention-driven video retrieval method by using an efficient spatiotemporal attention detection framework. Within the framework, we propose an efficient method for focus of attention (FOA) detection which involves combining adaptively the spatial and motion attention to form an overall attention map. Without computing motion explicitly, it detects motion attention using the rank deficiency of gray scale gradient tensors. We also propose an attention-driven shot matching method using primarily FOA. The matching method boosts the attended regions in the respective shots by converting attention values to importance factors in the process of shot similarity matching. Experiment results demonstrate the advantages of the proposed method in shot similarity matching. / Li, Shan. / "September 2007." / Adviser: Moon-Chuen Lee. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1108. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 150-168). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_344057 |
| Date | January 2007 |
| Contributors | Li, Shan, Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, theses |
| Format | electronic resource, microform, microfiche, 1 online resource (xiii, 168 p : ill.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
Page generated in 0.0021 seconds