In this paper, we present a clustering-based color indexing scheme for effective amd efficient image retrieval, which is essentially an exploration on the application of clustering technique to image retrieval. In our approach, the color features are clustered automatically using a color clustering algorithm twice (called two-level clustering processing) and two color feature summarizations are...

This paper presents a novel color image quantization algorithm. This algorithm improves color image quantization stability and accuracy using clustering ensemble. In our approach, we firstly adopt manifold single k-means clusterings for the color image to form a preliminary ensemble committee. Then, in order to avoid inexplicit correspondence among clustering groups, we use the original color v...

In this paper, one presents a fuzzy color clustering algorithm that is based on a new measure of similarity. This new measure of color similarity is defined on a perceptual color system called hsl. Keywords— Color similarity, fuzzy color clustering, hsl perceptual color system.

Scale-invariant feature transform (SIFT) transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information a...

Color is one of the properties which add information to the images. 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. Color based image segmentation using fuzzy c means and k means algorithms can be used for the clustering of color image. This method...

This paper presents simple yet powerful improvements in color reduction field, targeting interactive high-quality applications. Here maximum distance clustering (MDC) is used to initialize K-means clustering, which eliminates the drawback of clustering-based color reduction that tends to ignore colors with a small number of pixels. Maximum distance clustering’s speed problem due to the problem ...