Multimedia Content Analysis and Indexing for Filtering and Retrieval Applications

Dimitrova, Nevenka

doi:10.28945/605

Cited by 7 publications

(2 citation statements)

References 70 publications

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“…10 Colour, texture and shape are the extraction of low-level features. 13 Semantic content extraction is more complex because it is not only based on low-level features (colour, texture, shape, object, etc. ), i.e.…”

Section: Introductionmentioning

confidence: 99%

Kullback-Leibler similarity measures for effective content based video retrieval

Priya

Shanmugam

Bhaskaran

2013

The Imaging Science Journal

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Recent advancements in the multimedia technologies allow the capture and storage of video data with relatively inexpensive computers. As the necessity to query these data competently becomes significant, the amount of broadly accessible video data grows. As a result, content-based retrieval of video data turns out to be a demanding and vital problem. In this paper, an effective content-based video retrieval system is proposed. The raw video data are segmented into shots and the object feature, movement feature and the occlusion feature are extracted from these shots and the feature library is utilised for the storage process of these features. Subsequently, the Kullback-Leibler distance is computed among the features of the feature library and the features of the query clip which is extracted in the similar manner. Hence, with the aid of the Kullback-Leibler distance, the similar videos are extracted from the collection of videos based on the given query video clip in an effective manner.

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Section: Introductionmentioning

confidence: 99%

Kullback-Leibler similarity measures for effective content based video retrieval

Priya

Shanmugam

Bhaskaran

2013

The Imaging Science Journal

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“…Normally, a color space defines a one-to four-dimensional space [13]. Though, video will simply become an operative part of everyday computing environments when we can use it with the same ability that we currently use text [10]. Thus, CBVR is important information is automatically removed by using signal processing and pattern recognition techniques for audio and video signals, have been recently widely studied.…”

Section: Introductionmentioning

confidence: 99%

A Content Based Video Retrieval Analysis System with Extensive Features by Using Kullback-Leibler

Priya¹,

Shanmugam²,

Baskaran³

2014

IJCIS

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Content-based video retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based video retrieval, feature combination plays a key role. As a result content-based retrieval of all different type video data turns out to be a challenging and vigorous problem. This paper presents an effective content based video retrieval system, which recognizes and retrieves videos with three different types of visual effects. The raw video information is divided into shots and also the object feature, movement feature and also the occlusion options are extracted from these shots and also the feature library is used for the storage method of those options. Advanced on, the Kullback-Leibler distance is computed among the options of the feature library and also the options of the question clip that's extracted within the similar manner. The results show that it is possible to improve a system for content-based video retrieval by using Kullback-Leibler distance model, which takes careful consideration of the structure and distribution of visual features. Hence the final results with the aid of the Kullback-Leibler distance the similar videos are extracted from the collection of videos based on the given query video clip in an effective manner.

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A Survey of MPEG-1 Audio, Video and Semantic Analysis Techniques

Srinivasan

Pfeiffer

Nepal

et al. 2005

Multimed Tools Appl

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Multimedia Content Analysis and Indexing for Filtering and Retrieval Applications

Cited by 7 publications

References 70 publications

Kullback-Leibler similarity measures for effective content based video retrieval

Kullback-Leibler similarity measures for effective content based video retrieval

A Content Based Video Retrieval Analysis System with Extensive Features by Using Kullback-Leibler

A Survey of MPEG-1 Audio, Video and Semantic Analysis Techniques

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