Relevance feedback in content-based image and video retrieval

被引：3

作者：

Zhou, XS ^{[1
]}

Wu, Y ^{[1
]}

Cohen, I ^{[1
]}

Huang, TS ^{[1
]}

机构：

[1] Siemens Corp Res, Princeton, NJ 08540 USA

来源：

DIGITAL MEDIA: PROCESSING MULTIMEDIA INTERACTIVE SERVICES | 2003年

关键词：

D O I：

10.1142/9789812704337_0001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Compared to text databases, image and video databases are relatively newcomers. They offer new possibilities and new challenges. In particular, for images and video, it is possible to query by example and similarity in low-level features (color, texture, shape/structure, motion, and audio features in the case of video). We shall present algorithms for improving the performance of retrieval by relevance feedback from the user. Our approach is to consider the problem as two-category classification: Relevant and irrelevant classes. We shall also discuss: The use of unlabeled data in designing the classification algorithm; and the combined use of low-level features and keywords in querying. Some experimental results will be shown.

引用

页码：1 / 12

页数：12

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