This contribution describes using fuzzy c-means clustering method in image segmentation. Segmentation method is based on a basic region growing method and uses membership grades’ of pixels to classify pixels into appropriate segments. Images were in RGB color space, as feature space was used L*u*v* color space. Results were obtained on five color test images by experimental simulations in Matlab.

Image processing is an important research area in computer vision. Image segmentation plays the vital rule in image processing research. There exist so many methods for image segmentation. Clustering is an unsupervised study. Clustering can also be used for image segmentation. In this paper, an in-depth study is done on different clustering techniques that can be used for image segmentation wit...

The problem of color clustering is defined and shown to be a problem of assigning a large number (hundreds of thousands) of 3-vectors to a small number (256) of clusters. Finding those clusters in such a way that they best represent a full color image using only 256 distinct colors is a burdensome computational problem. In this paper, the problem is solved using "classical" techniques -k-means ...

One of the important requirements in image retrieval, indexing, classification, clustering and etc. is extracting efficient features from images. The color feature is one of the most widely used visual features. Use of color histogram is the most common way for representing color feature. One of disadvantage of the color histogram is that it does not take the color spatial distribution into con...

In this paper, a new algorithm is proposed through the clustering-based modeling and genetic algorithms for color-defects detection of ceramic tiles. The algorithm consists of two stages: feature extraction and inspection. In the former phase, the parameters of the algorithms are regulated by using one (some) reference defect-free ceramic tile image(s) in order to produce the contextual model o...

The problem of color clustering is defined and shown to be a problem of assigning a large number (hundreds of thousands) of 3-vectors to a small number (256) of clusters. Finding those clusters in such a way that they best represent a full color image using only 256 distinct colors is a burdensome computational problem. In this paper, the problem is solved using "classical" techniques -k-means ...

This paper describes a new approach to adapted estimation of parametric mixture model based on diagonal of the modified Riesz distribution (DMRD) defined in R , r ≥ 2. The DMRD can model accurately a withe variety of color image. This parameters index a family of distribution witch include the bivariate Gamma and the convolution product between bivariate Gamma and univariate Gamma. In our work,...

The main objective of this paper is to design An Intelligent Content Based Image Retrieval System for retrieving better results (similar images) from large image database with respect to an input query image. In this research work, we have used features like Color, Texture and shape with separate and combined effects. For making CBIR system intelligent, we have applied Non Linear Neural Network...

Color quantization is an important operation with numerous applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, K-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity...

This paper proposes a construction of a new distance in the HSL color space by a combination of the distances of HSL scalar components and by using the fuzzy cmeans framework to derive a fuzzy color clustering method. The main contribution consists in introducing two particular parameters to control the balance between the hue distance and the luminosity distance in the color Euclidean distance...