@inproceedings{db765981158946e49b23ea434cb61ec9,
title = "Saliency meets spatial quantization: A practical framework for large scale product search",
abstract = "Product image search aims to retrieve similar product images based on a query image. While deep learning based features work well in retrieving images of the same category (e.g. 'searching for T-shirts from all the clothing images'), they perform poorly when retrieving variants of images within the same category (e.g. 'searching for uniform of Chelsea football club from all T-shirts image'), since it requires fine grained matching on image details. In this paper, we present a spatial quantization approach that utilizes spatial pyramid pooling (SPP) and vector of locally aggregated descriptors (VLAD) to extract more discriminative features for style-aware product search. By using the proposed spatial quantization, spatial information is encoded into the image feature to improve the fine grained product image search. Finally, the experiments on a large scale real world dataset provided by Alibaba large-scale image search challenge (ALISC) demonstrate the effectiveness of our method.",
keywords = "Image retrieval, Salient region detection, Vector quantization",
author = "Shuhan Qi and Kyaw Zawlin and Zhang Hanwang and Wang Xuan and Gao Ke and Yao Lin and Chua, \{Tat Seng\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 ; Conference date: 11-07-2016 Through 15-07-2016",
year = "2016",
month = sep,
day = "22",
doi = "10.1109/ICMEW.2016.7574756",
language = "英语",
series = "2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016",
address = "美国",
}