Visible to the public Deep Progressive Hashing for Image Retrieval

TitleDeep Progressive Hashing for Image Retrieval
Publication TypeConference Paper
Year of Publication2017
AuthorsBai, Jiale, Ni, Bingbing, Wang, Minsi, Shen, Yang, Lai, Hanjiang, Zhang, Chongyang, Mei, Lin, Hu, Chuanping, Yao, Chen
Conference NameProceedings of the 2017 ACM on Multimedia Conference
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4906-2
Keywordscompositionality, deep hashing, expandability, hash algorithms, image retrieval, pubcrawl, Recurrent neural networks, Resiliency, saliency

This paper proposes a novel recursive hashing scheme, in contrast to conventional "one-off" based hashing algorithms. Inspired by human's "nonsalient-to-salient" perception path, the proposed hashing scheme generates a series of binary codes based on progressively expanded salient regions. Built on a recurrent deep network, i.e., LSTM structure, the binary codes generated from later output nodes naturally inherit information aggregated from previously codes while explore novel information from the extended salient region, and therefore it possesses good scalability property. The proposed deep hashing network is trained via minimizing a triplet ranking loss, which is end-to-end trainable. Extensive experimental results on several image retrieval benchmarks demonstrate good performance gain over state-of-the-art image retrieval methods and its scalability property.

Citation Keybai_deep_2017