Deep Supervised Auto-encoder Hashing for Image Retrieval
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.
Image retrieval Image hashing Supervised learning Deep neural network Convolutional auto-encoder
Sanli Tang Haoyuan Chi Jie Yang Xiaolin Huang Masoumeh Zareapoor
Institution of Image Processing and Pattern Recognition,Shanghai Jiao Tong University,Shanghai,China
国际会议
广州
英文
193-205
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）