NDLI: Simultaneous feature learning and hash coding with deep neural networks

Content Provider	IEEE Xplore Digital Library
Author	Hanjiang Lai Yan Pan Ye Liu Shuicheng Yan
Copyright Year	2015
Description	Author affiliation: Sch. of Software, Sun Yan-Sen Univ., Guangzhou, China (Yan Pan) \|\| Dept. of Electron. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Hanjiang Lai; Shuicheng Yan) \|\| Sch. of Inf. Sci. & Technol., Sun Yan-Sen Univ., Guangzhou, China (Ye Liu)
Abstract	Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.
Starting Page	3270
Ending Page	3278
File Size	362896
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298947
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Convolution Binary codes Image representation Training Visualization Quantization (signal) Architecture
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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