By Milan Petkovic, Willem Jonker
The region of content-based video retrieval is a really scorching region either for examine and for advertisement functions. with a view to layout powerful video databases for purposes akin to electronic libraries, video creation, and numerous net functions, there's a nice have to increase powerful options for content-based video retrieval. one of many major matters during this region of study is the best way to bridge the semantic hole among low-Ievel positive aspects extracted from a video (such as colour, texture, form, movement, and others) and semantics that describe video suggestion on a better point. during this e-book, Dr. Milan Petkovi6 and Prof. Dr. Willem Jonker have addressed this factor through constructing and describing a number of leading edge concepts to bridge the semantic hole. the most contribution in their study, that's the center of the publication, is the improvement of 3 strategies for bridging the semantic hole: (1) a method that makes use of the spatio-temporal extension of the Cobra framework, (2) a strategy in keeping with hidden Markov versions, and (3) a strategy in keeping with Bayesian trust networks. to judge functionality of those options, the authors have performed a couple of experiments utilizing actual video facts. The ebook additionally discusses domain names strategies as opposed to normal resolution of the matter. Petkovi6 and Jonker proposed an answer that permits a procedure to be utilized in a number of domain names with minimum alterations. additionally they designed and defined a prototype video database administration method, that is in keeping with strategies they proposed within the book.
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Extra info for Content-Based Video Retrieval: A Database Perspective
The content information has to be extracted from these multiple modalities through a process of interpretation, which relies on the achievements in the fields of computer vision, digital signal processing, artificial intelligence, and pattern recognition, among others. These fields, together with databases and information retrieval, provide fundamental building blocks for a contentbased retrieval system. To give an example of content-based video retrieval functionality, let us revisit our example movie database.
377-387.  C. J. Date, An Introduction to Database Systems, Addison-Wesley, third edition, 1985.  R. 0, Morgan Kaufmann, 1997.  E. Bertino, L. Martino, Objeet Oriented Database Systems, Concepts and Architeeture, Addison-Wesley, 1993.  P. N. L. Kersten, "Flattering an object algebra to provide performance", IEEE International Conference on Data Engineering, Orlando, 1998, pp. 568-577. P. de Vries, Content and Multimedia Database Management Systems, PhD Thesis, Centre for Telematics and Information Technology, Ensehede, the Netherlands, 1999.
The sampling is done with a fixed rate (usuaHy 11025, 22050, or 48000 times per seeond). e. a frame, represents the values of red, green, and blue of all pixels of a frame. A frame in a P AL video holds 768 x 576 pixels with a sampling rate of25 frames per second2 • Storage volume depends on the number ofbits used for encoding this information. 2GB per minute, and 72GB for an hour long video. Although compressing teehniques reduce these numbers for more than 10 times, video data is quite voluminous.
Content-Based Video Retrieval: A Database Perspective by Milan Petkovic, Willem Jonker