Robustness and Discrimination Oriented Hashing Combining Texture and Invariant Vector Distance

Abstract

Image hashing is a novel technology of multimedia processing with wide applications. Robustness and discrimination are two of the most important objectives of image hashing. Different from existing hashing methods without a good balance with respect to robustness and discrimination, which largely restrict the application in image retrieval and copy detection, i.e., seriously reducing the retrieval accuracy of similar images, we propose a new hashing method which can preserve two kinds of complementary features (global feature via texture and local feature via DCT coefficients) to achieve a good balance between robustness and discrimination. Specifically, the statistical characteristics in gray-level co-occurrence matrix (GLCM) are extracted to well reveal the texture changes of an image, which is of great benefit to improve the perceptual robustness. Then, the normalized image is divided into image blocks, and the dominant DCT coefficients in the first row/column are selected to form a feature matrix. The Euclidean distance between vectors of the feature matrix is invariant to commonly-used digital operations, which helps make hash more compact. Various experiments show that our approach achieves a better balance between robustness and discrimination than the state-of-the-art algorithms.