Robust Color Image Hashing With Nonnegative Matrix Factorization and Saliency Map for Copy Detection

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

Abstract: Image hashing is an effective technique for copy detection. This article proposes a robust color image hashing scheme with nonnegative matrix factorization (NMF) and saliency maps. The proposed hashing scheme constructs a weighted feature map via the saliency map and the matrix of color vector angles. It extracts hash from the weighted feature map by using 2-D discrete wavelet transform (DWT), NMF, and ordinal measurements. The use of 2-D DWT achieves an initial data reduction, the NMF provides a parts-based representation, and the ordinal measurements can make a short and robust sequence. These techniques jointly contribute to a robust, short, and discriminative hash. Experiments of classification and copy detection are both done. Comparative results indicate that the proposed hashing scheme outperforms the compared hashing schemes.