Commercial Video Retrieval With Video-Based Bag of Words

Wang, Jinqiao; Lu, Hanqing; Duan, Ling-Yu

doi:10.1142/9789812709677_0213

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…Ionu Mironica et al [18] have proposed a technique for Content-based retrieval in video databases had become an important task with the availability of large quantities of data in both public and proprietary archives. Most of video systems are based on feature classification, but problems appear because of "semantic gap" between high-level human concepts and the machine-readable low-level visual features.…”

Section: Review On Related Researchesmentioning

confidence: 99%

“…The figures of spectral components hold an excessive quantity of data of images 'marginal distributions and texture patterns, hence providing great discriminating power. Color feature is the most intuitive and obvious feature of the image, and generally adopt histograms to describe it [18]. Color histograms method has the advantages of speediness, low demand of memory space and not sensitive with the images' fluctuations of the size and rotation, it wins wide consideration accordingly.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Review On Related Researchesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Priya¹,

Shanmugam²,

Baskaran³

2014

IJCIS

View full text Add to dashboard Cite

show abstract

“…14 Frame-based approaches presume a set of key frames that can offer a compact representation of commercial video contents. 18 A frame that can represent the significant content of a video shot is known as a video key frame. 16 The key frames are directly extracted from the video sequence; therefore the added computational overhead is not necessary when compared with shot-based key frame extraction techniques.…”

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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Commercial Video Retrieval With Video-Based Bag of Words

Cited by 2 publications

References 11 publications

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

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

Kullback-Leibler similarity measures for effective content based video retrieval

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