RGB to Lab Transformation Using Image Segmentation

2017

Segmentation implies the division of an image into different objects or connected regions that do not overlap. Though, extensive research has been done in creating many different approaches and algorithms for image segmentation, however, it is still not very clear to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images [7]. A reliable and accurate segmentation of an image is, in general, very difficult to achieve by purely automatic means. Present researches on image segmentation using clustering algorithms reveals that K-means clustering algorithm so far produces best results but some improvements can be made to improve the results. The biggest disadvantage of our heavy usage of k-means clustering, is that it means we would have to think of a k each time, which really doesn’t make too much sense because we would like to algorithm to solve this on his own....

Image segmentation divides an image into several constituent components such as color, structure, shape, and texture. It forms a major research topic for many image processing researchers as the applications are endless. Its applications include image enhancement, object detection, image retrieval, image compression, and medical image processing to name a few. The segmentation of color images is necessary for efficient pattern recognition and feature extraction involving various color spaces such as RGB, HSV and CIE L*A*B*, etc. This paper describes the different cluster based segmentation techniques used for segmenting the different color images and the resultant is analyzed with subjective and objective measures. Initially, registered color images are considered as input. Then the cluster based segmentation techniques namely K-Means clustering, Pillar-Kmeans clustering and Fuzzy C-means (FCM) clustering techniques are applied. Further, the segmented image is analyzed with measures such as compactness and execution time. From the experimental results, it has been observed that K-means and Pillar-Kmeans are the most suitable techniques for RGB, HSV and LAB color spaces than the FCM technique.

Segmentation is a fundamental process in digital image processing which has found extensive applications in areas such as medical image processing, compression, diagnosis arthritis from joint image, automatic text hand writing analysis, and remote sensing.The clustering methods can be used to segment any image into various clusters based on the similarity criteria like color or texture. In this research we have developed a method to segment color images using K-means clustering algorithm. K-means clustering algorithm divides the image into K clusters based on the similarity between the pixels in that cluster. In this research we have used Euclidean distance formula to define clusters in K-mean clustering. The proposed method has been applied to a variety of images and conclusions have been drawn.

1991

Because segmentation results of grey-value images might be negatively a ected by the presence of intensity changes due to shadows and surface curvature in those images, we have investigated to what extent these segmentation results can be improved by using color information. The in uence of di erent color systems has been examined as well as two segmentation methods were investigated: a clustering and a region growing technique. The clustering technique is based on the k-means algorithm, and is iterative. The region growing technique is derived from the split&merge algorithm proposed by 4].In the region growing technique, homogeneity criteria play an important role. Two homogeneity criteria have been implemented: a homogeneity criterion based on functional approximation and the mean&variance homogeneity criterion. The results show a reasonable insensibility to changes in intensity due to shadows and surface curvature, whereby the region growing technique produces better results than the clustering algorithm. Color systems which are linear combinations of the same RGB basis provide similar segmentation results. The IHS, rgb and the L a b color systems provide poor segmentation results when intensity is small. The rgb color system provides the best segmentation results if the image contains intensity changes.

Image segmentation is a method to divide an image into various segments which are homogeneous in nature. Though there are so many methods available for this purpose, but the use of data clustering technique in the segmentation of images has emerged in recent times. For the segmentation of color images the color space also plays a vital role. In this paper authors have applied K-means clustering algorithm in L*a*b color space to segment the color images. The Authors have also analyzed the effect of variation on the number of cluster formation on the segmentation of color images.

Color image segmentation is currently a very emerging topic for researchers in Image processing. Clustering is a frequently chosen methodology for this image segmentation task. But for a better segmentation, there arises the need of an optimal technique. In this paper, we propose an integrated approach for color image segmentation which is a new of its kind. Here, we integrate the famous k-means algorithm with watershed algorithm. But, here we chose ‘cosine’ distance measure for k-means algorithm to optimize the segmented result of the later one. Also, as color space has a leading impact on color image segmentation task, so, we chose HSV color space for our proposed approach. Since usually the noise arises during the segmentation process, so here the final segmented image is filtered by median filter to make the output image clearer and noise free. The result of the proposed approach is found to be quite satisfactory.

International Journal of Applied Evolutionary Computation, 2015

The findings of image segmentation reflects its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms normally used in image segmentation – the typical K-Means, its modified K-Means++ and their proposed Enhanced Clustering method. The idea is to present a brief explanation of the fundamental working principles implicated in these methods. They have analyzed the performance criterion which affects the outcome of segmentation by considering two vital quality measures namely – Structural Content (SC) and Root Mean Square Error (RMSE) as suggested by Jaskirat et al., (2012). Experimental...

2016

A comparative analysis of color image segmentation of three different techniques is presented in this paper with different noise levels. The robustness of each algorithm is checked on the basis of its accuracy of the clusters mapped. We have taken K-Means, FCM and Density based image segmentation approach for the analysis. In these methods of segmentation, the objects are distinguished clearly from the background. Basically Image is that type of information which has to be processed effectively and correctly. Segmentation of an image requires the separation or division of the image into regions of similar and multiple attribute. Image segmentation assigns a tag to each and every pixel in an image such that each pixel with the same tag share certain visual characteristic. Basic attribute for segmentation of an image is its luminance amplitude for an image and color components for a color image and its intensity level. Clustering is one of the methods which are used for segmentation. ...

Image segmentation is an important and interesting digital image pre-processing phase to enhance the performance of various pattern recognition and computer vision applications. Segmentation process enhance images analysis through the extractions of features from the relevance part of image only. In this paper, a comparative study between five different color segmentation techniques is performed. The experimental results of PSNR and MSE metrics show that K-means clustering algorithm has better results when compared to the other algorithms, but still need to be modified to deal with different types of sharp and smooth edges.

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 is used to cluster and measure accuracy of the color images by segmenting each color pixels in the color images. Once segmentation is done, the fuzzy c means method is used for creating membership operation functions to define the degree to which a pixel belongs to an edge or a uniform region. The k-means clustering is used to partition n data points into k clusters. This unsupervised clustering approaches has a strong affinity to get trapped into local minima while generating an optimal solution. Hence, it makes clustering wholly dependent on the distribution of primary cluster centre. This research work is employed to find the distance between color pixels of the RGB color spaces. Implementation has been done using MATLAB Simulation tool which generates the better result of this clustering algorithms.