Robust Hashing With Local Tangent Space Alignment for Image Copy Detection

Xiaoping Liang, Zhenjun Tang, Xianquan Zhang, Mengzhu Yu, Xinpeng Zhang

Abstract: Robust hashing is a useful technique for the image applications of watermarking, authentication, quality assessment and copy detection. This article proposes a new robust hashing for image copy detection by using local tangent space alignment (LTSA). A key contribution is the weighted visual map computation based on the difference of Gaussian (DOG) and visual attention model. The weighted visual map can provide the proposed method with good robustness. Another contribution is the feature learning via LTSA from the feature matrix of the weighted visual map in discrete cosine transform domain. As it can maintain the local geometric relationships within image, the learned features can make the proposed method discriminative. Extensive experiments on public databases are conducted to validate the proposed robust hashing method. Compared with some famous robust hashing methods, the proposed robust hashing method demonstrates preferable classification performance in terms of discrimination and robustness. Copy detection performance is tested and the result verifies effectiveness of the proposed robust hashing method.