Pseudo relevance feedback based on iterative probabilistic one-class SVMs in web image retrieval

被引：0
|
作者：
He, Jingrui [1 ]
Li, Mingjing [2 ]
Li, Zhiwei [2 ]
Zhang, Hong-Jiang [2 ]
Tong, Hanghang [1 ]
Zhang, Changshui [1 ]
机构：
[1] Automation Department, Tsinghua University, Beijing 100084, China
[2] Microsoft Research Asia, 49 Zhichun Road, Beijing 100080, China
来源：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2004年 / 3332卷
关键词：
Image enhancement - Image retrieval - Iterative methods - Search engines - Support vector machines;
D O I：
10.1007/978-3-540-30542-2_27
中图分类号：
学科分类号：
摘要：
To improve the precision of top-ranked images returned by a web image search engine, we propose in this paper a novel pseudo relevance feedback method named iterative probabilistic one-class SVMs to re-rank the retrieved images. By assuming that most top-ranked images are relevant to the query, we iteratively train one-class SVMs, and convert the outputs to probabilities so as to combine the decision from different image representation. The effectiveness of our method is validated by systematic experiments even if the assumption is not well satisfied. © Springer-Verlag Berlin Heidelberg 2004.
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页码：213 / 220
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