Image Similarity Using Sparse Representation and Compression Distance

TL;DR

This paper introduces a novel image similarity measure based on sparse representations, which improves upon traditional compression-based methods by effectively capturing image similarities for clustering, retrieval, and classification tasks.

Contribution

It proposes a sparse representation approach to measure image similarity, addressing limitations of existing compression-based methods in the image domain.

Findings

High accuracy in image clustering

Effective image retrieval performance

Improved image classification results

Abstract

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.