Feature fusion based learning for image hashing
With the exponential growth of online multimedia, image copyright protection now confronts the issue of large scale.In this paper, we propose a feature fusion based learning method for image hashing, aiming to achieve efficient image copy protection.It first effectively utilizes the correlation between different feature models, and then generates compact fingerprints for image representation, which prevents huge semantic loss during the process of hashing.To accurately map image features into the Hamming space, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error.Therefore, our hashing method not only preserves the local structure of individual feature, but also globally considers the local structures for all the features to learn a group of hash functions.The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.
Image Hashing Feature Fusion Image copy detection large scale
YAN Lingyu LING Hefei OU Yinyu
School of Computer Science, Hubei University of Technology, Wuhan, 430068;School of Computer Science School of Computer Science and Technology, Huazhong University of Science and Technology,Wuhan 43007
国内会议
武汉
英文
403-412
2015-03-28（万方平台首次上网日期，不代表论文的发表时间）