A robust and accurate hue descriptor that is useful in modeling human color perception and for computer vision applications is explored. The hue descriptor is based on the peak wavelength of a Gaussian-like function (called a wraparound Gaussian) and is shown to correlate as well as CIECAM02 hue to the hue designators of papers from the Munsell and Natural Color System color atlases and to the hue names found in Moroney's Color Thesaurus. The new hue descriptor is also shown to be significantly more stable under a variety of illuminants than CIECAM02. The use of wraparound Gaussians as a hue model is similar in spirit to the use of subtractive Gaussians proposed by Mizokami et al., but overcomes many of their limitations.

In this paper, we propose a new algorithm for extending the hierarchical clustering methods and introduce a Multi-View Agglomerative Clustering approach to handle multi-view represented objects. Experiments on real world datasets indicate that our algorithm considering the relationship among multiple views can provide a solution with improved quality in multi-view setting. We find empirically that the multi-view version of our Agglomerative Clustering, independent of linkage method and given any number of views, greatly improves on its single-view counterparts.