Robust Image Hash Function Based on Polar Harmonic Transforms and Feature Selection

Robust hashing aims at representing the perceptual essence of media data in a compact manner, and it has been widely applied in content identification, copyright protection, content authentication, etc. In this paper, a robust hashing algorithm is proposed by incorporating the polar harmonic transforms and feature selection. The proposed hashing algorithm starts by preprocessing, where morphological operations are employed to disclose the principle structures of the input image. The polar harmonic transforms, which have shown promising results in pattern classification, are then exploited to produce candidate features for hash computation. In order to select the most robust and discriminative features, feature selections are applied on the candidate feature set via boosting algorithm. The hash string is finally generated by randomly permuting the quantization indexes of selected features. Experimental results reveal that the proposed work is both distortion-resistent and discriminative, and it can achieve higher content identification accuracy than the comparative algorithm.