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liuw:RAP

Wei Liu, Wei Jiang, Shih-Fu Chang. Relevance Aggregation Projections for Image Retrieval. In ACM International Conference on Image and Video Retrieval, Niagara Falls, Canada, July 2008.

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Abstract

To narrow the semantic gap in content-based image retrieval (CBIR), relevance feedback is utilized to explore knowledge about the userí»s intention in finding a target image or a image category. Users provide feedback by marking images returned in response to a query image as relevant or irrelevant. Existing research explores such feedback to refine querying process, select features, or learn a image classifier. However, the vast amount of unlabeled images is ignored and often substantially limited examples are engaged into learning. In this paper, we address the two issues and propose a novel effective method called Relevance Aggregation Projections (RAP) for learning potent subspace projections in a semi-supervised way. Given relevances and irrelevances specified in the feedback, RAP produces a subspace within which the relevant examples are aggregated into a single point and the irrelevant examples are simultaneously separated by a large margin. Regarding the query plus its feedback samples as labeled data and the remainder as unlabeled data, RAP falls in a special paradigm of imbalanced semi-supervised learning. Through coupling the idea of relevance aggregation with semi-supervised learning, we formulate a constrained quadratic optimization problem to learn the subspace projections which entail semantic mining and therefore make the underlying CBIR system respond to the userí»s interest accurately and promptly. Experiments conducted over a large generic image database show that our subspace approach outperforms existing subspace methods for CBIR even with few iterations of user feedback

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Wei Liu
Wei Jiang
Shih-Fu Chang

BibTex Reference

@InProceedings{liuw:RAP,
   Author = {Liu, Wei and Jiang, Wei and Chang, Shih-Fu},
   Title = {Relevance Aggregation Projections for Image Retrieval},
   BookTitle = {ACM International Conference on Image and Video Retrieval},
   Address = {Niagara Falls, Canada},
   Month = {July},
   Year = {2008}
}

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