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New techniques for content-based ima...

Chen, Yu.

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New techniques for content-based image/video retrieval, classification, and analysis.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	New techniques for content-based image/video retrieval, classification, and analysis./
Author:	Chen, Yu.
Description:	97 p.
Notes:	Adviser: Edward K. Wong.
Contained By:	Dissertation Abstracts International63-02B
Subject:	Computer Science -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3041689
ISBN:	0493550593

New techniques for content-based image/video retrieval, classification, and analysis.
Chen, Yu.

New techniques for content-based image/video retrieval, classification, and analysis. - 97 p.

Adviser: Edward K. Wong.

Thesis (Ph.D.)--Polytechnic University, 2002.

In this dissertation, we explored and developed new techniques and methods for image/video retrieval and classification. Various low- and high-level features have been developed. These include augmented histogram, colorfulness, most prominent color, regions computed from DCT coefficients, focus of attention, motion-activity, motion-magnitude, and cut rate. These features, together with other features such as straight lines, text, and human faces form a powerful set of low- and high-level features for image/video retrieval and classification. Their effectiveness is demonstrated in a content-based image retrieval simulation experiment we performed, a knowledge-based video classification prototype system, and a hyper-linked video retrieval prototype system we implemented. We have also explored the use of straightline features for video sub-classification, and camera transform estimation for basketball games.

ISBN: 0493550593Subjects--Topical Terms:

890869
Computer Science

New techniques for content-based image/video retrieval, classification, and analysis.
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020 $a 0493550593
035 $a (UnM)AAI3041689
035 $a AAI3041689
040 $a UnM $c UnM
100 1 $a Chen, Yu. $3 1260328
245 1 0 $a New techniques for content-based image/video retrieval, classification, and analysis.
300 $a 97 p.
500 $a Adviser: Edward K. Wong.
500 $a Source: Dissertation Abstracts International, Volume: 63-02, Section: B, page: 0872.
502 $a Thesis (Ph.D.)--Polytechnic University, 2002.
520 $a In this dissertation, we explored and developed new techniques and methods for image/video retrieval and classification. Various low- and high-level features have been developed. These include augmented histogram, colorfulness, most prominent color, regions computed from DCT coefficients, focus of attention, motion-activity, motion-magnitude, and cut rate. These features, together with other features such as straight lines, text, and human faces form a powerful set of low- and high-level features for image/video retrieval and classification. Their effectiveness is demonstrated in a content-based image retrieval simulation experiment we performed, a knowledge-based video classification prototype system, and a hyper-linked video retrieval prototype system we implemented. We have also explored the use of straightline features for video sub-classification, and camera transform estimation for basketball games.
520 $a The augmented histogram we developed captures information about the “spatial distribution” of pixels, in addition to the intensity or color count. Since the spatial information is computed globally in terms of relative distance between pixels, it is insensitive to image rotation and translation. The knowledge-based prototype system we developed uses a rule-based implementation to classify video into one of five possible classes. The rules capture human knowledge on how to classify video. We present experimental results to demonstrate the effectiveness of this approach. The hyper-linked video retrieval system is based on the concept of human déjà vu. We present a prototype system called DejaVideo, which uses visual similarity to find similar shots
590 $a School code: 0179
650 $a Computer Science $3 890869
690 $a 098
710 2 $a Polytechnic University $3 1260284
773 0 $t Dissertation Abstracts International $g 63-02B
790 $a 017
790 1 $a Wong, Edward K., $e adviso
791 $a Ph.D
792 $a 200
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3041689