Discrete Hash Image Retrieval Algorithm with High-Level Semantic Features
DUAN Wenjing;CHEN Shaoping;School of Science, Wuhan University of Technology;
Deep Hash has been applied in the field of image search very well. However, the previous deep Hash method has the limitation that the semantic information is not fully utilized. This paper, develops a discrete Hash algorithm based on deep supervision, assuming that learning binary code should be an ideal choice of classification. The pair tag information and classified information are used to learn Hash codes within a framework. The output of the last layer is restricted to binary code directly. Due to the discrete properties of Hash codes, the alternate minimization method is used to optimize the target function. The proposed algorithm is proved to be better than the other supervised Hash methods in three image retrieval databases CIFAR-10,NUS-WIDE and SUN397.
【CateGory Index】： TP391.41;TP181