  In this paper, a novel perceptual image hashing scheme for color images is
proposed based on ring-ribbon quadtree and color vector angle. First, original
image is subjected to normalization and Gaussian low-pass filtering to produce
a secondary image, which is divided into a series of ring-ribbons with
different radii and the same number of pixels. Then, both textural and color
features are extracted locally and globally. Quadtree decomposition (QD) is
applied on luminance values of the ring-ribbons to extract local textural
features, and the gray level co-occurrence matrix (GLCM) is used to extract
global textural features. Local color features of significant corner points on
outer boundaries of ring-ribbons are extracted through color vector angles
(CVA), and color low-order moments (CLMs) is utilized to extract global color
features. Finally, two types of feature vectors are fused via canonical
correlation analysis (CCA) to prodcue the final hash after scrambling. Compared
with direct concatenation, the CCA feature fusion method improves
classification performance, which better reflects overall correlation between
two sets of feature vectors. Receiver operating characteristic (ROC) curve
shows that our scheme has satisfactory performances with respect to robustness,
discrimination and security, which can be effectively used in copy detection
and content authentication.
