Image Similarity Using Sparse Representation and Compression Distance
This addresses the challenge of image similarity measurement for computer vision applications, but it is incremental as it adapts existing compression-based ideas to the image domain.
The paper tackled the problem of measuring image similarity using compression-based methods, which previously performed poorly for images, by proposing a sparse representation approach that encodes image information relative to another image and uses sparsity as a compressibility measure, achieving high accuracies in image clustering, retrieval, and classification.
A new line of research uses compression methods to measure the similarity between signals. Two signals are considered similar if one can be compressed significantly when the information of the other is known. The existing compression-based similarity methods, although successful in the discrete one dimensional domain, do not work well in the context of images. This paper proposes a sparse representation-based approach to encode the information content of an image using information from the other image, and uses the compactness (sparsity) of the representation as a measure of its compressibility (how much can the image be compressed) with respect to the other image. The more sparse the representation of an image, the better it can be compressed and the more it is similar to the other image. The efficacy of the proposed measure is demonstrated through the high accuracies achieved in image clustering, retrieval and classification.