By Milan Petkovic, Willem Jonker
The zone of content-based video retrieval is a really sizzling zone either for examine and for advertisement purposes. for you to layout powerful video databases for purposes equivalent to electronic libraries, video creation, and a number of net purposes, there's a nice have to improve potent suggestions for content-based video retrieval. one of many major concerns during this sector 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 inspiration on a better point. during this ebook, Dr. Milan Petkovi6 and Prof. Dr. Willem Jonker have addressed this factor by means of constructing and describing numerous cutting edge recommendations to bridge the semantic hole. the most contribution in their study, that is the middle of the ebook, is the advance of 3 concepts for bridging the semantic hole: (1) a strategy that makes use of the spatio-temporal extension of the Cobra framework, (2) a method in keeping with hidden Markov types, and (3) a strategy in line with Bayesian trust networks. to judge functionality of those concepts, the authors have carried out a few experiments utilizing genuine video facts. The publication additionally discusses domain names strategies as opposed to basic resolution of the matter. Petkovi6 and Jonker proposed an answer that enables a process to be utilized in a number of domain names with minimum alterations. in addition they designed and defined a prototype video database administration approach, that is in keeping with options they proposed within the book.
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Extra info for Content-Based Video Retrieval: A Database Perspective
F . f . f Amato etal. f . f . f . f Decleir et al. f Media Streams ? - - - - - - 4. 1 summarizes the important characteristics of major contributions to the field with regard to video modeling. Performance measures of the systems based on described models are not included, since, to the best of our knowledge, the systems have never been tested on the same data sets. 1, makes c1ear two important things. 1) use automatically extracted features to represent the video content, but they do not provide semantics that describe high-level concepts of a video, such as objects and events.
Sundaram, D. Zhong, "A Fully Automated Content Based Video Search Engine Supporting Spatio-Temporal Queries", IEEE Transaction on Circuits and Systemsfor Video Technology, Vol. 8, No. , 1998.  T. T. Kuo, A. P. Chen, "A Content-Based Query Language for Video Databases", Proc. ofIEEE Multimedia Computing Systems, 1996.  G. Amato, G. Mainetto, P. Savino, "An Approach to a Content-Based Retrieval of Multimedia Data" Multimedia Tools and applications, Vol. 7, No. 1/2, 1998, pp. 5-36. M. htm  D.
Tbe concept layers are on the top. They consist of logical concepts that are subject of interest to users or applications. Automatie mapping from the raw video data layer to the feature layer has been already achieved to a certain extent, but automatie mapping from the feature to the concept layers is still a ehallenging problem. We divide the concept layer into two ontologically different layers: the object and the event layer. 46 Content-Based Video Retrieval: A Database Perspective The object layer consists of entities (logical concepts), characterized by a prominent spatial dimension and assigned to regions across frames.