Zhang, Jun and Ye, Lei, 2009, Local aggregation function learning based on support vector machines, Zhang, J. & Ye, L. (2009). Local aggregation function learning based on support vector machines. Signal Processing, 89 (11), 2291-2295., 89(11), 2291-2295.
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.