Spectral Clustering of Images in LUV Color Space by Spatial-Color Pixel Classification

Abstract

This work is based on color image segmentation by spatial-color pixel classification in Luv color space. Classes of pixels are difficult to be identified when the color distributions of the different objects highly overlap in the color space and when the color points give rise to non-convex clusters. It is proposed to apply spectral classification to regroup the pixels which represent the same regions, into classes. Spectral clustering achieves a spectral decomposition of a similarity matrix in order to construct an eigen-space in which the clusters are expected to be well separated. The similarity matrix used in this paper is derived from a spatial-color compactness function. This function takes into account both the distribution of colors in the color space and the spatial location of colors in the image plane. Spectral clustering that uses FCM performs better in Luv color space when compared with other Spectral clustering algorithms..

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

“…Moreover, these clustering techniques may not scale to large datasets because of their computational time [24] and low accuracy. Therefore, many cluster analysis techniques are being developed for specific practical problems [25,26], such as finding classes of genes that have similar functions, grouping information on the Internet for different specific queries, and clustering biomedical data [27,28]. Likewise, new clustering approaches specific to visual domains in high dimensional space are required in order to produce better results.…”