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Local aggregation function learning based on support vector machines
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DOI：10.1016/j.sigpro.2009.04.034.png)
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In content-based image retrieval (CBIR), feature aggregation is an approach to obtain image similarity by combining multiple feature distances. Most existing feature aggregation methods focus on heuristic-based or linear combination functions, which cannot sufficiently explore the interdependencies between features. Instead, a single aggregation function is always applied to all query images without considering the special features of each query image. In this paper, aggregation is formulated as a classification problem in a feature similarity space and solved by support vector machines (SVMs). The new method can learn an aggregation function for each query image and extend the linear aggregation to a nonlinear one using the kernel trick. Experiments demonstrate that the image retrieval performance of the proposed method is superior. (C) 2009 Elsevier B.V. All rights reserved.
Keyword:
Content-based image retrieval
Feature fusion
Optimizing learning
Support vector machine
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