Fast communication: Local aggregation function learning based on support vector machines

Authors:
Jun Zhang;Lei Ye
Affiliations:
School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW 2522, Australia;School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Venue:
Signal Processing
Year:
2009

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Abstract

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.