Video Mining

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Springer Science & Business Media, Aug 31, 2003 - Computers - 340 pages
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Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video Video understanding deals with understanding of video understanding. sequences, e.g., recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvi ous overlap with computer vision. The main goal of computer graphics is to generate and animate realistic looking images, and videos. Re searchers in computer graphics are increasingly employing techniques from computer vision to generate the synthetic imagery. A good exam pIe of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is derived from real images using computer vision techniques. Here the shift is from synthesis to analy sis followed by synthesis. Image processing has always overlapped with computer vision because they both inherently work directly with images.
 

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Contents

EFFICIENT VIDEO BROWSING Using Multiple Synchronized Views
1
BEYOND KEYFRAMES THE PHYSICAL SETTING AS A VIDEO MINING PRIMITIVE
31
TEMPORAL VIDEO BOUNDARIES
61
VIDEO SUMMARIZATION USING MPEG7 MOTION ACTIVITY AND AUDIO DESCRIPTORS A Compressed Domain Approach to Video Brows...
91
MOVIE CONTENT ANALYSIS INDEXING AND SKIMMING VIA MULTIMODAL INFORMATION
123
VIDEO OCR A SURVEY AND PRACTITIONERS GUIDE
155
VIDEO CATEGORIZATION USING SEMANTICS AND SEMIOTICS
185
UNDERSTANDING THE SEMANTICS OF MEDIA
219
STATISTICAL TECHNIQUES FOR VIDEO ANALYSIS AND SEARCHING
253
UNSUPERVISED MINING OF STATISTICAL TEMPORAL STRUCTURES IN VIDEO
279
PSEUDORELEVANCE FEEDBACK FOR MULTIMEDIA RETRIEVAL
309
Index
339
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Page 346 - Lessons Learned from the Creation and Deployment of a Terabyte Digital Video Library.

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