Online Metric Learning for Relevance Feedback in E-Commerce Image Retrieval Applications |
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Authors: | Hong Gu 顾弘 Guangzhou Zhao 赵光宙 Jun Qiu 裘君 |
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Affiliation: | aCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;bNingbo Institute of Technology, Zhejiang University, Ningbo 315100, China |
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Abstract: | Relevance feedback plays a key role in multiple feature-based image retrieval applications. This paper describes an online metric learning approach for a set of ranking functions. In the feedback round, the most relevant and most nonrelevant images related to the target image are selected to construct a relative comparison triplet. The weighting parameters of the multiple ranking functions are updated by minimizing a quadratic objective function constrained by the triplet. The approach unifies the learning algorithm for the most commonly used ranking functions. Thus, multiple features with their own ranking function can easily be employed in the ranking module without feature reconstruction. The method is computationally inexpensive and appropriate for large-scale e-commerce image retrieval applications. Customized ranking functions are well supported. Practically, simplified ranking functions yield better results when the number of query rounds is relatively small. Experiments with an image dataset from a real e-commerce platform show the superiority of the proposed approach. |
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Keywords: | metric learning image ranking relevance feedback relative comparison |
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