Robust video hashing based on radial projections of key frames

Robust signal hashing defines a feature vector that characterizes the signal, independently of "nonsignificant" distortions of its content. When dealing with images, the considered distortions are typically due to compression or small geometrical manipulations. In other words, robustness means that images that are visually indistinguishable should produce equal or similar hash values. To discriminate image contents, a hash function should produce distinct outputs for different images. Our paper first proposes a robust hashing algorithm for still images. It is based on radial projection of the image pixels and is denoted the Radial hASHing (RASH) algorithm. Experimental results provided on the USC-SIPI dataset reveal that the proposed RASH feature vector is more robust and provides much stronger discrimination than a conventional histogram-based feature vector. The RASH vector appears to be a good candidate to build indexing algorithms, copy-detection systems, or content-based authentication mechanisms. To take benefit from the RASH vector capabilities, video content is summarized into key frames, each of them characterizing a video shot and described by its RASH vector. The resulting video hashing system works in real time and supports most distortions due to common spatial and temporal video distortions.