Segmentation of color images based on the gravitational clustering concept

A new clustering algorithm derived from the Markovian model of the gravitational clustering concept is proposed that works in the RGB measurement space for color image. To enable the model to be applicable in image segmentation, the new algorithm imposes a clustering constraint at each clustering iteration to control and determine the formation of multiple clusters. Using such constraint to limit the attraction between clusters, a termination condition can be easily defined. The new clustering algorithm is evaluated objectively and subjectively on three different images against the K-means clustering algorithm, the recursive histogram clustering algorithm for color (also known as the multi-spectral thresholding), the Hedley-Yan algorithm, and the widely used seed-based region growing algorithm. From the evaluation, it is observed that the new algorithm exhibits the following characteristics: (1) its objective measurement figures are comparable with the best in this group of segmentation algorithms; (2) it generates smoother region boundaries; (3) the segmented boundaries align closely with the original boundaries; and (4) it forms a meaningful number of segmented regions. © 1998 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

Enhanced K-means Color Clustering Based on SLIC Superpixels Merging incorporated within the Entomology Software: AInsectID

Superpixel-based segmentation is an important pre-processing step for the simplification of image processing. The subjective nature behind the determination of optimal cluster numbers in segmentation algorithms can result in either underor over-segmentation burdens, depending on the image type. Insect wings, with their intricate color patterns, pose significant challenges for the accurate capture of color diversity in clustering algorithms, assuming a spherical and isotropic cluster distribution is used. This paper introduces a hybrid approach for color clustering in insect wings, integrating the Simple Linear Iterative Clustering (SLIC) method to generate the initial superpixels, and a DeltaE 2000 function the precisely discriminated merging of superpixels. Color differences between superpixels serve to measure homogeneity during the merging process. The proposed new algorithm demonstrates enhanced segmentation as it overcomes the issue of over-segmentation and under-segmentation, as evidenced by the results derived from the Boundary Recall, Rand index, Under-segmentation Error, and Bhattacharyya distance using ground truth data. The Silhouette score and Dunn Index are also used to quantitatively evaluate the efficacy of our new proposed clustering technique.<br/

Unsupervised Segmentation Method for Diseases of Soybean Color Image Based on Fuzzy Clustering

The method of color image segmentation based on Fuzzy C-Means (FCM) clustering is simple, intuitive and is to be implemented. However, the clustering performance is affected by the center point of initialization and high computation and other issues. In this research, we propose a new color image unsupervised segmentation method based on fuzzy clustering. This method combines advantages of the fuzzy C-means algorithm and unsupervised clustering algorithm. Firstly, by gradually changing clusters c, and according to validity measurement, it can unsupervised search for optimal clusters c; then in order to achieve higher accuracy of clustering effect, the distance measurement scale was improved. In our experiments, this method was applied to color image segmentation for three kinds of soybean diseases. The results show that this method can more accurately segment the lesion area from the color image, and the segmentation processing of soybean disease is ideal, robustness, and have a high accuracy

Study and Development of Some Novel Image Segmentation Techniques

Some fuzzy technique based segmentation methods are studied and implemented and some fuzzy c means clustering based segmentation algorithms are developed in this thesis to suppress high and low uniform random noise. The reason for not developing fuzzy rule based segmentation method is that they are application dependent In many occasions, the images in real life are affected with noise. Fuzzy c means clustering based segmentation does not give good segmentation result under such condition. Various extension of the FCM method for segmentation are present in the literature. But most of them modify the objective function hence changing the basic FCM algorithm present in MATLAB toolboxes. Hence efforts have been made to develop FCM algorithm without modifying their objective function for better segmentation . The fuzzy technique based segmentation methods that are studied and developed are summarized here. (A) Fuzzy edge detection based segmentation: Two fuzzy edge detection methods are studied and implemented for segmentation: (i) FIS based edge detection and (ii) Fast multilevel fuzzy edge detector (FMFED). (i): The Fuzzy Inference system (FIS) based edge detector consists of some fuzzy inference rules which are defined in such a way that the FIS system output (“edges”) is high only for those pixels belonging to edges in the input image. A robustness to contrast and lightining variations were also taken into consideration while developing these rules.The output of the FIS based edge detector is then compared with the existing Sobel, LoG and Canny edge detector results. The algorithm is seen to be application dependent and time consuming. (ii) Fast Multilevel Fuzzy Edge Detector: To realise the fast and accurate detection of edges, the FMFED algorithm is proposed. It first enhances the image contrast by means of a fast multilevel fuzzy enhancement algorithm using simple transformation function based on two image thresholds. Second, the edges are extracted from the enhanced image by using a two stage edge detector operator that identifies the edge candidates based on local characteristics of the image and then determines the true edge pixels using edge detector operator based on extremum of the gradient values. Finally the segmentation of the edge image is done by morphological operator by edge linking. (B) FCM based segmentation: Two fuzzy clustering based segmentation methods are developed: (i) Modified Spatial Fuzzy c-Means (MSFCM) (ii) Neighbourhood Attraction Fuzzy c-Means (NAFCM). . (i) Contrast-Limited Adaptive Histogram Equalization Fuzzy c-Means (CLAHEFCM): This proposed algorithm presents a color segmentation process for low contrast images or unevenly illuminated images. The algorithm presented in this paper first enhances the contrast of the image by using contrast limited adaptive histogram equalization. After the enhancement of the image this method divides the color space into a given number of clusters, the number of cluster are fixed initially. The image is converted from RGB color space to LAB color space before the clustering process. Clustering is done here by using Fuzzy c means algorithm. The image is segmented based on color of a region, that is, areas having same color are grouped together. The image segmentation is done by taking into consideration, to which cluster a given pixel belongs the most. The method has been applied on a number of color test images and it is observed to give good segmentation results (ii) Modified Spatial Fuzzy c-means (MSFCM): The proposed algorithm divides the color space into a given number of clusters, the number of cluster are fixed initially. The image is converted from RGB color space to LAB color space before the clustering process. A robust segmentation technique based on extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed. The spatial information of each pixel in an image has been taken into consideration to get a noise free segmentation result. The image is segmented based on color of a region, that is, areas having same color are grouped together. The image segmentation is done by taking into consideration, to which cluster a given pixel belongs the most. The method has been applied to some color test images and its performance has been compared to FCM and FCM based methods to show its superiority over them. The proposed technique is observed to be an efficient and easy method for segmentation of noisy images. (iv) Neighbourhood Attraction Fuzzy c Means Algorithm: A new algorithm based on the IFCM neighbourhood attraction is used without changing the distance function of the FCM and hence avoiding an extra neural network optimization step for the adjusting parameters of the distance function, it is called Neighborhood Atrraction FCM (NAFCM). During clustering, each pixel attempts to attract its neighbouring pixels towards its own cluster. This neighbourhood attraction depends on two factors: the pixel intensities or feature attraction, and the spatial position of the neighbours or distance attraction, which also depends on neighbourhood structure. The NAFCM algorithm is tested on a synthetic image (chapter 6, figure 6.3-6.6) and a number of skin tumor images. It is observed to produce excellent clustering result under high noise condition when compared with the other FCM based clustering methods

Bayesian weighted K-Means clustering algorithm as applied to cotton trash measurement

Image segmentation is one of most difficult tasks in computer vision. It plays a critical role in object recognition of natural images. Unsupervised classification, or clustering, represents one promising approach for solving the image segmentation problem in typical application environments. The K-Means and Bayesian Learning algorithms are two well-known unsupervised classification methods. The K-Means approach is computationally efficient, but assumes imaging conditions which are unrealistic in many practical applications. While the Bayesian learning technique always produces a theoretically optimal segmentation result, the large, computational burden it requires is often unacceptable for many industrial tasks. A novel clustering algorithm, called Bayesian Weighted K-Means, is proposed in this thesis. Through simplification of the Bayesian learning approach\u27s decision-making process using cluster weights, the new technique is able to provide approximately optimal segmentation results while maintaining the computational efficiency generally associated with the K-means algorithm. The capabilities of this new algorithm are demonstrated using both synthetic images with controlled amounts of noise, and real color images of cotton lint contaminated with non-lint material

Perceptual color clustering for color image segmentation based on CIEDE2000 color distance

In this paper, a novel technique for color clustering with application to color image segmentation is presented. Clustering is performed by applying the k-means algorithm in the L*a*b* color space. Nevertheless, Euclidean distance is not the metric chosen to measure distances, but CIEDE2000 color difference formula is applied instead. K-means algorithm performs iteratively the two following steps: assigning each pixel to the nearest centroid and updating the centroids so that the empirical quantization error is minimized. In this approach, in the first step, pixels are assigned to the nearest centroid according to the CIEDE2000 color distance. The minimization of the empirical quantization error when using CIEDE2000 involves finding an absolute minimum in a non-linear equation and, therefore, an analytical solution cannot be obtained. As a consequence, a heuristic method to update the centroids is proposed. The proposed algorithm has been compared with the traditional k-means clustering algorithm in the L*a*b* color space with the Euclidean distance. The Borsotti parameter was computed for 28 color images. The new version proposed outperformed the traditional one in all cases

Applying New Method for Computing Initial Centers of k-Means Clustering with Color Image Segmentation

          As a classic clustering method, the traditional k-Means algorithm has been widely used in image processing and computer vision, pattern recognition and machine learning. It is known that the performance of the k-means clustering algorithm depends highly on initial cluster centers. Generally initial cluster centers are selected randomly, so the algorithm could not lead to the unique result. In this paper, we present a method to compute initial centers for k-means clustering. Our method based on an efficient technique for estimating the modes of a distribution. We apply the new method in segmentation phase of color images. The experimental results appeared quite satisfactory

Applying New Method for Computing Initial Centers of k-Means Clustering with Color Image Segmentation

          As a classic clustering method, the traditional k-Means algorithm has been widely used in image processing and computer vision, pattern recognition and machine learning. It is known that the performance of the k-means clustering algorithm depends highly on initial cluster centers. Generally initial cluster centers are selected randomly, so the algorithm could not lead to the unique result. In this paper, we present a method to compute initial centers for k-means clustering. Our method based on an efficient technique for estimating the modes of a distribution. We apply the new method in segmentation phase of color images. The experimental results appeared quite satisfactory

New techniques for data clustering and color image segmentation

The objectives of this work are twofold: (1) to create an improved automatic clustering procedure that produces results consistent with manual clustering of data points by humans; and (2) to find an improved technique for automatic segmentation of images. First, we developed a clustering technique using an M-ART (Mahalanobis distance-based Adaptive Resonance Theory) neural network. The "vigilance" p in the M-ART network affects the maximum size of clusters, and consequently affects the number of clusters. Normally the "optimal" value of p is heavily data dependent and therefore can only be chosen by users and adjusted by trial-and-error. We propose a procedure to automatically adjust the value of p based on a pre-defined required separation between clusters, which is data independent and can be determined beforehand. Experiments conducted on synthetic multidimensional and texture datasets demonstrate the effectiveness and reliability of the proposed method. Segmentation is the process of partitioning a digital image into multiple segments or non-overlapping regions. Partitioning an image into non-overlapping regions assures that pixels in each region share the same visual properties, such as color or texture, while pixels in different regions exhibit significant differences in these features. We found that M-ART works well only with convex-shaped clusters (segments) that are sufficiently separated, which is not the case for typical real-scene images. Accordingly, we investigated and presented developing a more advanced general purpose image segmentation method, called the DUHO method. This DUHO algorithm contains two main steps. First, the superpixel generating algorithm is applied to a given image to build K superpixels. Then a new region growing algorithm iteratively groups these superpixels into appropriate regions and forms the final image segmentation result. The proposed method is a type of unseeded region-based segmentation that preserves the spatial relationship between pixels in the image, and hence preserves the detailed edges and the image spatial structure. A quantitative evaluation method based on square color error is introduced, and experiments with real datasets, consisting of 300 color images of natural scenes from the available data, show very good results from our DUHO method when compared with results from the well-known segmentation methods

Color image segmentation using a spatial k-means clustering algorithm

This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm