Pseudo Relevance Feedback Based on Iterative Probabilistic One-Class SVMs in Web Image Retrieval
作者： Jingrui He , Mingjing Li , Zhiwei Li , Hong-Jiang Zhang , Hanghang Tong
DOI: 10.1007/978-3-540-30542-2_27
关键词:
摘要: To improve the precision of top-ranked images returned by a web image search engine, we propose in this paper novel pseudo relevance feedback method named iterative probabilistic one-class SVMs to re-rank retrieved images. By assuming that most are relevant query, iteratively train SVMs, and convert outputs probabilities so as combine decision from different representation. The effectiveness our is validated systematic experiments even if assumption not well satisfied.
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