Hefei Ling1, Yang Fang1, Lei Wu1, Ping Li1,*, Jiazhong Chen1, Fuhao Zou1, Jialie Shen2
CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 85-100, 2019, DOI:10.32604/cmc.2019.05588
Abstract Recently, Convolutional Neural Network (CNN) based hashing method has achieved its promising performance for image retrieval task. However, tackling the discrepancy between quantization error minimization and discriminability maximization of network outputs simultaneously still remains unsolved. Motivated by the concern, we propose a novel Balanced Deep Supervised Hashing (BDSH) based on variant posterior probability to learn compact discriminability-preserving binary code for large scale image data. Distinguished from the previous works, BDSH can search an equilibrium point within the discrepancy. Towards the goal, a delicate objective function is utilized to maximize the discriminability of the output space More >
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