Robust Image Hashing With Isomap and Saliency Map for Copy Detection
Xiaoping Liang, Zhenjun Tang, Jingli Wu, Zhixin Li, Xinpeng Zhang
Abstract
Compression technology for representing image is on demand for efficiently processing images in the Big Data era. Image hashing is an effective compression technology for computing a short representation based on visual content of input image. Currently, most reported image hashing algorithms have weakness in making a desirable classification between discrimination and robustness and thus can not reach good performance in copy detection. To address these issues, this paper proposes a new robust image hashing with Isometric Mapping (Isomap) and saliency map for copy detection. A key contribution is hash generation with saliency map determined by the Frequency Tuned (FT) method, which can guarantee robustness of the proposed image hashing. Another contribution is the use of Isomap in deriving hash from the FT-based saliency map. Since Isomap can discover the internal geometry features of image, the use of Isomap can learn discriminative image features and thus discrimination of the proposed image hashing is ensured. Experiments on open image databases are carried out. Comparison results illustrate that the proposed image hashing is better than some state-of-the-art algorithms in the performances of classification and copy detection.