Color Image Segmentation Algorithm Research Articles

Colour Space Selection for Unsupervised Colour Image Segementation by Analysis of Connectedness Properties

In this article, we propose a new colour image segmentation algorithm by unsupervised classification of pixels. This procedure iteratively constructs the classes by histogram multithresholding. For this purpose, the procedure selects different colour spaces in which the pixels belonging to the modes of the 1D-histograms present remarkable connectedness properties, so that each mode corresponds effectively to a region in the image.

Contrast-Based Color Image Segmentation

In this letter, we propose a color image segmentation algorithm based on contrast information. Given a color image, we use contrast information, instead of the commonly used derivative information, to detect edges. To fit for human's visual perception, the CIE L/sup */a/sup */b/sup */ color space is used and the /spl Delta/E/sub ab/ color difference is adopted as the measure of color contrast. A subjective experiment is made to demonstrate the weak correlation between the perceived color contrast and the levels of (L/sup */,a/sup */,b/sup */). This experiment implies the feasibility of using a single-threshold scheme to suppress perceptually faint boundaries. A complete segmentation scheme is proposed and the simulation results demonstrate the superiority of this approach in providing reasonable and reliable color image segmentation.

STILL IMAGE SEGMENTATION TOOLS FOR OBJECT-BASED MULTIMEDIA APPLICATIONS

In this paper, a color image segmentation algorithm and an approach to large-format image segmentation are presented, both focused on breaking down images to semantic objects for object-based multimedia applications. The proposed color image segmentation algorithm performs the segmentation in the combined intensity–texture–position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. A preprocessing stage of conditional image filtering and a modified K-Means-with-connectivity-constraint pixel classification algorithm are used to allow for seamless integration of the different pixel features. Unsupervised operation of the segmentation algorithm is enabled by means of an initial clustering procedure. The large-format image segmentation scheme employs the aforementioned segmentation algorithm, providing an elegant framework for the fast segmentation of relatively large images. In this framework, the segmentation algorithm is applied to reduced versions of the original images, in order to speed-up the completion of the segmentation, resulting in a coarse-grained segmentation mask. The final fine-grained segmentation mask is produced with partial reclassification of the pixels of the original image to the already formed regions, using a Bayes classifier. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

Color image segmentation and parameter estimation in a markovian framework

An unsupervised color image segmentation algorithm is presented, using a Markov random field (MRF) pixel classification model. We propose a new method to estimate initial mean vectors effectively even if the histogram does not have clearly distinguishable peaks. The only parameter supplied by the user is the number of classes.

Color image segmentation based on 3-D clustering: morphological approach

In this paper, a new segmentation algorithm for color images based on mathematical morphology is presented. Color image segmentation is essentially a clustering process in 3-D color space, but the characteristics of clusters vary severely, according to the type of images and color coordinates. Hence, the methodology employs the scheme of thresholding the difference of Gaussian smoothed 3-D histogram to get the initial seeds for clustering, and then uses a closing operation and adaptive dilation to extract the number of clusters and their representative values, and to include the suppressed bins during Gaussian smoothing, without a priori knowledge on the image. This procedure also implicitly takes into account the statistical properties, such as the shape, connectivity and distribution of clusters. Intensive computer simulation has been performed and the results are discussed in this paper. The results of the simulation show that the proposed segmentation algorithm is independent of the choice of color coordinates, the shape of clusters, and the type of images. The segmentation results using the k-means technique are also presented for comparison purposes.

Histogram-based segmentation in a perceptually uniform color space

In this work, we present a segmentation algorithm for color images that uses the watershed algorithm to segment either the two-dimensional (2-D) or the three-dimensional (3-D) color histogram of an image. For compliance with the way humans perceive color, this segmentation has to take place in a perceptually uniform color space like the Luv space. To avoid oversegmentation, the watershed algorithm has to be applied to a smoothed histogram.

Color Image Segmentation Using Entropy of Fuzziness and Markov Random Field

This paper presents a color image segmentation algorithm using the fuzzy entropy and Markov Random Fields (MRFs). During an iterative clustering, the fuzzy entropy uses the S-function membership and a suitable window-size vector to minimize the ambiguity of the R, G and B conditional histograms. The main 3D cluster is detected. A local processing clusters and labels the definite pixels coarsely, and masks the ambiguous ones. These ambiguous pixels are further assigned and labeled globally to clusters using the membership function of fuzzy_c means (FCM). As some pixels are misclassified, MRF’s use iterated conditional mode (ICM) to refine the segmentation.

Adaptive Bayesian segmentation of color images

An adaptive Bayesian segmentation algorithm for color images is presented, which extends the adaptive clustering approach of Pappas to multichannel images. A scalar segmentation label field is generated for the multichannel data, which is modeled as a vector field, where the components of the vector field (each individual channel) are assumed to be conditionally independent given the segmentation labels. The class conditional probability model for the vector image field is taken as a multivariate Gaussian with a space-varying mean function. A Gibbs random field is employed as the a priori probability model for the segmentation label field that imposes a spatial connectivity constraint on the labels. The space-varying class means associated with the image segments can be used to form an estimate of the actual image from noisy observations. Experimental results are provided to demonstrate the benefits of using adaptivity via the space-varying means and the spatial connectivity constraint. We also discuss the effects of the color space within which the clustering is performed on resulting segmentations.

Multiresolution color image segmentation

Image segmentation is the process by which an original image is partitioned into some homogeneous regions. In this paper, a novel multiresolution color image segmentation (MCIS) algorithm which uses Markov random fields (MRF's) is proposed. The proposed approach is a relaxation process that converges to the MAP (maximum a posteriori) estimate of the segmentation. The quadtree structure is used to implement the multiresolution framework, and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore, maximize the MAP estimate. The multiresolution scheme enables the use of different dissimilarity measures at different resolution levels. Consequently, the proposed algorithm is noise resistant. Since the global clustering information of the image is required in the proposed approach, the scale space filter (SSF) is employed as the first step. The multiresolution approach is used to refine the segmentation. Experimental results of both the synthesized and real images are very encouraging. In order to evaluate experimental results of both synthesized images and real images quantitatively, a new evaluation criterion is proposed and developed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A segmentation algorithm for color images

A method that combines clustering and region merging for color image segmentation is presented. We start with clustering based on recursive one-dimensional histogram analysis, where the parameter that controls the segmentation process has been set to produce an oversegmentation. The resulting regions are then merged on the basis of a criterion that takes into account color similarity and spatial proximity.

Segmentation of color images using spatial and color space information

One of the first steps needed to extract information from images for most machine vision applications is the segmentation of the image. We present a new segmentation algorithm for color images that combines both color space and spatial information. The algorithm is oriented to images that should exhibit clustering of the color space data, such as images of paper-based maps. The algorithm separates edge pixels from those in smooth regions and applies different segmentation algorithms to each group. The pixels in smooth regions are used to segment the color space using a histogram analysis technique. These regions are then grown into the edge regions to classify the edge pixels. The algorithm is robust and fast, as verified by experimental results.

On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques

In this paper, a segmentation algorithm for color images based on the thresholding and the fuzzy c-means (FCM) techniques is presented. The scale-space filter is used as a tool for analyzing the histograms of three color components. The methodology uses a coarse-fine concept to reduce the computational burden required for the FCM. The coarse segmentation attempts to segment coarsely using the thresholding technique, while the fine segmentation assigns the pixels, which remain unclassified after the coarse segmentation, to the closest class using the FCM. Attempts also have been made to compare the performance of the proposed algorithm with other existing algorithms—Ohlander's, Rosenfeld's, and Bezdek's. Intensive computer simulation has been performed and the results are discussed in this paper. The simulation results indicate that the proposed algorithm yields the most accurate segmented image on the color coordinate proposed by Ohta et al., while requiring a reasonable amount of computational effort.