A Novel Image perceptual hashing algorithm based on frequency decomposition and LoG

The perceptual hashing (pHash) algorithm generate a unique sequence of image. The similarity of images can be determined by comparing the distance between the hash sequences. A novel pHash methods is proposed in this paper.Firstly, the image after pre-processing is decomposed by NSCT into high-frequency and low-frequency parts, and the Zernike moments of high-frequency and LBP features of low-frequency are extracted. Secondly, extract the perceptual hashing features of the pre-processing image by using the LoG operator. Finally, the three feature sequences are concatenated to obtain the hash sequence of the image. Experimental results show that the proposed method outperforms other popular pHash algorithms in terms of uniqueness, differentiation, and robustness which means it can improve the effect of image retrieval.