Learning Regularized, Query-Dependent Bilinear Similarities for Large Scale Image Retrieval

Zhanghui Kuang, Jian Sun, Kwan-Yee K. Wong; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 413-420

Abstract


An effective way to improve the quality of image retrieval is by employing a query-dependent similarity measure. However, implementing this in a large scale system is non-trivial because we want neither hurting the efficiency nor relying on too many training samples. In this paper, we introduce a query-dependent bilinear similarity measure to address the first issue. Based on our bilinear similarity model, query adaptation can be achieved by simply applying any existing efficient indexing/retrieval method to a transformed version (surrogate) of a query. To address the issue of limited training samples, we further propose a novel angular regularization constraint for learning the similarity measure. The learning is formulated as a Quadratic Programming (QP) problem and can be solved efficiently by a SMO-type algorithm. Experiments on two public datasets and our 1-million web-image dataset validate that our proposed method can consistently bring improvements and the whole solution is practical in large scale applications.

Related Material


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[bibtex]
@InProceedings{Kuang_2013_CVPR_Workshops,
author = {Kuang, Zhanghui and Sun, Jian and Wong, Kwan-Yee K.},
title = {Learning Regularized, Query-Dependent Bilinear Similarities for Large Scale Image Retrieval},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2013}
}