We present measurements of the color and luminosity dependence of galaxy clustering at z ∼ 1 in the DEEP2 Galaxy Redshift Survey. Using volume-limited subsamples in bins of both color and luminosity, we find that: 1) The clustering dependence is much stronger with color than with luminosity and is as strong with color at z ∼ 1 as is found locally. We find no dependence of the clustering amplitu...

A novel approach to color image segmentation is proposed and formulated in this paper. Conventional color segmentation methods apply SOFMs – among other techniques – as a first stage clustering in hierarchical or hybrid schemes in order to achieve color reduction and enhance robustness against noise. 2-D SOFMs defined upon 3-D color space are usually employed to render the distribution of color...

\Ve consider the problem of color image quantization, or clustering of the color space. vVe propose a new methodology for doing this, called model-based clustering trees. This is grounded in model-based clustering, which bases inference on finite mixture models estimated by maximum likelihood using the EM algorithm, and automatically chooses the number of clusters by Bayesian model selection, a...

In this paper we present scalable algorithms for image retrieval based on color. Our solution for scalability is to cluster the images in the database into groups of images with similar color content. At search time the query image is first compared with the pre-computed clusters, and only the closest set of clusters is further examined by comparing the query image to the images in that set. Th...

Color-vision-based applications for mobile phones has become a subject of special interest lately. It would be interesting to investigate an unsupervised, adaptive, and fast algorithm that can classify color components into color clusters. We propose a hierarchical clustering approach using a single-linkage algorithm and a k-means clustering approach to color classification for color-based imag...

The ability to characterize the color content of natural imagery is an important application of image processing. The pixel by pixel coloring of images may be viewed naturally as points in color space, and the inherent structure and distribution of these points affords a quantization, through clustering, of the color information in the image. In this paper, we present a novel topologically driv...

In this paper we consider the application of the Self Organizing Map to the adaptive computation of cluster representatives (codevectors) over non-stationary data. The paradigm of Non-stationary Clustering is represented by the problem of Color Quantization of image sequences. Experimental results on the Color Quantization of an image sequence show the extreme robustness of the SOM as an adapti...

This paper has a further exploration and study of visual feature extraction. Image retrieval based on multi-feature fusion is achieved by using normalized Euclidean distance classifier. According to the HSV (Hue, Saturation, Value) color space, the work of color feature extraction is finished, the process is as follows: quantifying the color space in non-equal intervals, constructing one dimens...

In this paper, an approach to the segmentation of microscopic color images is addressed, and applied to medical images. The approach combines a clustering method and a region growing method. Each color plane is segmented independently relying on a watershed based clustering of the plane histogram. The marginal segmentation maps intersect in a label concordance map. The latter map is simplified ...

Background subtraction is a vital step in many computer vision systems. In background subtraction, one is given two (or more) frames of a video sequence taken with a still camera. Due to the stationarity of the camera, any color change in the scene is mainly due to the presence of moving objects. The goal of background subtraction is to separate the moving objects (also called the foreground) f...