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Rapid image retrieval with binary hash codes based on deep learning
Author(s): GuangWei Deng; Cheng Xu; XiaoHan Tu; Tao Li; Nan Gao
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Paper Abstract

With the ever-growing large-scale image data on the web, rapid image retrieval has become one of the hot spots in the multimedia field. And it is still very difficult to reliable image retrieval due to the complex image appearance variations. Inspired by the robustness of convolutional neural networks features, we propose an effective deep learning framework to generate compact similarity-preserving binary hash codes for rapid image retrieval. Our main idea is incorporating deep convolutional neural network (CNN) into hash functions to jointly learn feature representations and mappings from them to hash codes. In particular, our approach which learns hash codes and image representations takes pairs of images as training inputs. Meanwhile, an effective loss function is used to maximize the differentiability of the output space by encoding the supervised information from the input image pairs. We extensively evaluate the retrieval performance on two large-scale datasets CIFAR-10 and NUS-WIDE, and the evaluation shows that our method gives a better performance than traditional hashing learning methods in image retrieval.

Paper Details

Date Published: 26 July 2018
PDF: 6 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 1082813 (26 July 2018); doi: 10.1117/12.2502072
Show Author Affiliations
GuangWei Deng, Hunan Univ. (China)
Cheng Xu, Hunan Univ. (China)
XiaoHan Tu, Hunan Univ. (China)
Tao Li, Hunan Univ. (China)
Nan Gao, Hunan Univ. (China)


Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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