Image clustering using a similarity measure incorporating human perception

Clustering similar images is an important task in image processing and computer vision. It requires a measure to quantify pairwise similarities of images. The performance of clustering algorithm depends on the choice of similarity measure. In this paper, we investigate the effectiveness of data-independent (distance-based), data-dependent (mass-based)and hybrid (dis)similarity measures in the image clustering task using three benchmark image collections with different sets of features. Our results of K-Medoids clustering show that uses the hybrid Perceptual Dissimilarity Measure (PMD)produces better clustering results than distance-based ℓp-norm and mass-based mp-dissimilarity.