Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:ICMR - Proc. ACM Int. Conf. Multimed. Retr.
Abstract:We present a novel Deep Supervised Hashing with code operation (DSOH) method for large-scale multi-label image retrieval. This approach is in contrast with existing methods in that we respect both the intention gap and the intrinsic multilevel similarity of multi-labels. Particularly, our method allows a user to simultaneously present multiple query images rather than a single one to better express her intention, and correspondingly a separate sub-network in our architecture is specifically designed to fuse the query intention represented by each single query. Furthermore, as in the training stage, each image is annotated with multiple labels to enrich its semantic representation, we propose a new margin-adaptive triplet loss to learn the fine-grained similarity structure of multi-labels, which is known to be hard to capture. The whole system is trained in an end-to-end manner, and our experimental results demonstrate that the proposed method is not only able to learn useful multilevel semantic similarity-preserving binary codes but also achieves state-of-the-art retrieval performance on three popular datasets. © 2018 ACM.
Translation or Not:no
Date of Publication:2018-06-05
Co-author:Song, Ge
Correspondence Author:Song, Ge,Tan Xiaoyang