ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (8): 1824-1832.doi: 10.7544/issn1000-1239.2017.20170197

Special Issue: 2017人工智能前沿进展专题

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Multi-Scale Deep Learning for Product Image Search

Zhou Ye, Zhang Junping   

  1. (School of Computer Science, Fudan University, Shanghai 200433) (Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433)
  • Online:2017-08-01

Abstract: Product image search is an important application of mobile visual search in e-commerce. The target of product image search is to retrieve the exact product in a query image. The development of product image search not only facilitates people’s shopping, but also results in that e-commerce moves forward to mobile users. As one of the most important performance factors in product image search, image representation suffers from complicated image background, small variance within each product category, and variant scale of the target object. To deal with complicated background and variant object scale, we present a multi-scale deep model for extracting image representation. Meanwhile, we learn image similarity from product category annotations. We also optimize the computation cost by reducing the width and depth of our model to meet the speed requirements of online search services. Experimental results on a million-scale product image dataset shows that our method improves retrieval accuracy while keeps good computation efficiency, comparing with existing methods.

Key words: product image search, deep learning, multi scale, metric learning, model compression

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