Abstract
With the high-growing of e-commerce, more and more users have changed to buy from websites rather than in stores. To deal with mass products, the traditional text-based product search has become incompetent to meet use’s requirement. In this paper, we explore deep learning with convolutional neural networks (CNN) to resolve query’s classification, and propose an efficient approach for product image search. For a query image, we first train a CNN model of a large database containing various product images to discriminate the query’s category. Then we search similar products from the established category and utilize these visual results to parse the query with attribute. Finally we use the extracted attribute tags to finish the textual re-ranking and obtain the most relevant retrieved product list. Experimental evaluation shows that our approach significantly outperforms state of art in product image search.
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References
Huang, J., et al.: Cross-domain image retrieval with a dual attribute-aware ranking network. In: IEEE International Conference on Computer Vision, pp. 1062–1070. IEEE (2015)
Chen, J.C., Liu, C.F.: Visual-based deep learning for clothing from large database. In: ASE Bigdata & Socialinformatics. ACM (2015)
Lin, K., et al.: Rapid clothing retrieval via deep learning of binary codes and hierarchical search. In: ACM on International Conference on Multimedia Retrieval, pp. 499–502. ACM (2015)
Liu, S., et al.: Hi, magic closet, tell me what to wear! In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 1333–1334. ACM (2012)
Nguyen, T.V., et al.: Sense beauty via face, dressing, and/or voice. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 239–248. ACM (2012)
Bossard, L., Dantone, M., Leistner, C., Wengert, C., Quack, T., Gool, L.V.: Apparel classification with style. In: ACCV (2012)
Wei, D., Catherine, W., Anurag, B., Robinson, P., Neel, S.: Style finder: fine-grained clothing style recognition and retrieval. In: CVPRW (2013)
Tseng, C.H., Hung, S.S., Tsay, J.J.: An efficient garment visual search based on shape context. In: Proceedings of the 9th WSEAS International Conference on Multimedia Systems and Signal Processing. World Scientific and Engineering Academy and Society (WSEAS) (2009)
Mizuochi, M., Kanezaki, A., Harada, T.: Clothing retrieval based on local similarity with multiple images. In: Proceedings of the ACM International Conference on Multimedia, pp. 1165–1168. ACM (2014)
Yamaguchi, K.: Parsing clothing in fashion photographs. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3570–3577 (2012)
Qian, X., et al.: Landmark summarization with diverse viewpoints. IEEE Trans. Circuits Syst. Video Technol. 25(11), 1857–1869 (2015)
Qian, X., Tan, X., Zhang, Y., Hong, R., Wang, M.: Enhancing sketch-based image retrieval by re-ranking and relevance feedback. IEEE Trans. Image Process. 25(1), 195–208 (2016)
Qian, X., Zhao, Y., Han, J.: Image location estimation by salient region matching. IEEE Trans. Image Process. 24(6), 4348–4358 (2015)
Yang, X., Qian, X., Xue, Y.: Scalable mobile image retrieval by exploring contextual saliency. IEEE Trans. Image Process. 24(6), 1709–1721 (2015)
Yang, X., Qian, X., Mei, T.: Learning salient visual word for scalable mobile image retrieval. Pattern Recogn. 48(10), 3093–3101 (2015)
Lu, D., Liu, X., Qian, X.: Tag based image search by social re-ranking. IEEE Trans. Multimedia (2016). doi:10.1109/TMM.2016.2568099
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Zhou, X., Zhang, Y., Bai, X., Zhu, J., Zhu, L., Qian, X. (2016). Product Image Search with Deep Attribute Mining and Re-ranking. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_55
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DOI: https://doi.org/10.1007/978-3-319-48896-7_55
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