Image similarity measurement from sparse reconstruction errors

This paper presents a new approach to measuring the similarity between two images using sparse reconstruction. Our approach alleviates the difficulty of selecting and extracting suitable features from images which usually requires domain-specific knowledge. The proposed measure, the Sparse SNR (SSNR), does not use any prior knowledge about the data type or the application. SSNR is generic in the sense that it is applicable, without modification, to a variety of problems involving different types of images. Given a pair of images, a set of basis vectors (dictionary) is learnt for each image such that each image can be represented as a linear combination of a small number of its dictionary elements. Each image is reconstructed by two dictionaries - the one trained on the image itself and the second - trained on the other image. We develop a novel similarity measure based on the resulting reconstruction errors. To the best of our knowledge, this is the first attempt to develop a sparse reconstruction-based similarity measure. Excellent classification, clustering and retrieval results are achieved on benchmark datasets involving facial images and textures.