Deep Supervised Auto-encoder Hashing for Image Retrieval

Sanli Tang, Haoyuan Chi, Jie Yang, Xiaolin Huang, Masoumeh Zareapoor

Abstract: Image hashing approaches map high dimensional images to compact binary codes that preserve similarities among images. Although the image label is important information for supervised image hashing methods to generate hashing codes, the retrieval performance will be limited according to the performance of the classifier. Therefore, an effective supervised auto-encoder hashing method (SAEH) is proposed to generate low dimensional binary codes in a point-wise manner through deep convolutional neural network. The auto-encoder structure in SAEH is designed to simultaneously learn image features and generate hashing codes. Moreover, some extra relaxations for generating binary hash codes are added to the objective function. The extensive experiments on several large scale image datasets validate that the auto-encoder structure can indeed increase the performance for supervised hashing and SAEH can achieve the best image retrieval results among other prominent supervised hashing methods.