Large Scale Fashion Search System with Deep Learning and Quantization Indexing

Published in SoICT 2018, 2018


Recently, the problems of clothes recognition and clothing item retrieval have attracted a number of researchers due to its practical and potential values to real-world applications. The task is to automatically find relevant clothing items given a single user-provided image without any extra metadata. Most existing systems mainly focus on clothes classification, attribute prediction, and matching the exact in-shop items with the query image. However, these systems do not mention the problem of latency period or the amount of time that users have to wait when they query an image until the query results are retrieved. In this paper, we propose a fashion search system, which automatically recognizes clothes and suggests multiple similar clothing items with an impressively low latency. Through extensive experiments, it is verified that our system outperforms all existing systems in term of clothing item retrieval time.


@inproceedings{dinh2018large, title={Large Scale Fashion Search System with Deep Learning and Quantization Indexing}, author={Dinh, Thoi Hoang and Van, Toan Pham and Thanh, Ta Minh and Thanh, Hau Nguyen and Hoang, Anh Pham}, booktitle={Proceedings of the Ninth International Symposium on Information and Communication Technology}, pages={106–113}, year={2018} }