COLOR IMAGE SEGMENTATION TECHNIQUES: A SURVEY

In computer vision and image processing, still image segmentation is a relevant research area due to its wide spread usage and application. With the improvement of computer processing capabilities and the increased application of color image, researchers are more concerned about color image segmentation. Color image segmentation methods can be seen as an extension of the gray scale image segmentation method in the color images, but many of the original gray scale image segmentation methods cannot be directly applied to color images. This requires improving the method of original gray image segmentation method according to the color image which has the feature of rich information or research a new image segmentation method it specially used in color image segmentation.This paper provides a survey of achievements, problems being encountered and usage of the techniques in different areasof color image segmentation. This Literature review helps researcher to understand various techniques, themes, methodologies, approaches and controversies so for applied for color image 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.

Image segmentation can be a traditional theme in the field of image processing as well as is a hot spot while focusing involving image processing methods. While using the improvement of personal computer processing abilities plus the improved application of color image, the actual color image segmentation are more and more implicated from the scientists. Color image segmentation techniques is visible as an expansion in the gray image segmentation method within the color graphics, many the main gray image segmentation methods is not immediately put on color photos. This involves to improve this method involving unique gray image segmentation method using the color image that are fitted with the feature involving plenteous information or perhaps search a whole new image segmentation method this especially utilised in color image segmentation. This paper presents a review on different image segmentation techniques and a comparative study on these techniques.

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.

2014

segmentation is a low level operation concerned with partitioning of images by determining similarity or discontinuity , or equivalently , by finding edges or boundaries’. image segmentation is the process of partitioning an image into multiple partitions, so as to change the epitomization of an image into something that is more meaningful and easier to analyze . Several general-purpose algorithms and techniques have been developed for image segmentation. This paper trace the different segmentation techniques used for multichannel images. Firstly this paper investigates and compiles some of the technologies used for image segmentation, which are well suited for gray scale images as well as multichannel images. Afterwards a bibliographical survey of currently utilizing color models for segmentation of multichannel images techniques is given in this paper then a comparative analysis of different methods is done and finally general tendencies in image segmentation are presented.

Image segmentation is one of the popular methods in the field of Image processing. It is the process of grouping an image into units that are consistent with respect to one or more characteristics. Segmentation in gray images has lots of methods and it has several algorith ms to represent it. But images giving more information in scenes i.e., colour images have few numbers of methods to segment. So, this paper represent colour image segmentation methods in the literature and getting to prepare novel segmentation method with combined form of masking, thresholding and noise removal methods. Otsu method is one of the best and classical Thresholding method used in colour image segmentation. It uses various combinations of masks to scan over the image to detect the correct boundar y. Otsu method divide the segmentation tasks in two or more modules and make the process easily. In the same way this paper discusses about fuzzy membership functions mask to scan the image with few combinations and include noise removal method to produce the output image in well defined manner

Eighth International Multi-Conference on Systems, Signals & Devices, 2011

In this paper, entropy and between-class variance based thresholding methods for color images segmentation are studied. The maximization of the between-class variance (MVI)and the entropy (ME) have been used as a criterion functions to determine an optimal threshold to segment images into nearly homogenous regions. Segmentation results from the two methods are validated and the segmentation sensitivity for the test data available is evluated, and a comparative study between these methods in different color spaces is presented. The experimental results demonstrate the superiority of the MVI method for color image segmentation.

Neurocomputing, 2018

Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown.

2010

Color image segmentation is still a challenging problem. Literature reveals many supervised algorithms wherein the primary input is the number of segments to which the image is to be segmented. Currently researchers are focusing on unsupervised segmentation algorithms. The main advantage of the proposed method is that no a priori information is required to segment the given color image and hence considered as an unsupervised approach. The proposed method is found to be reliable and works satisfactorily on different kinds of color images. Subjective comparison and objective evaluation shows the efficacy of the proposed method over other existing methods.

In field of image processing, image segmentation plays an important role that focus on splitting the whole image into segments. Representation of an image so that it can be more easily analysed and involves more information is an important segmentation goal. The process of partitioning an image can be usually realized by Region based, Boundary based or edge based method. In this work a hybrid approach is followed that combines improved bee colony optimization and Tabu search for color image segmentation. The results produced from this hybrid approach are compared with non-sorted particle swarm optimization, non-sorted genetic algorithm and improved bee colony optimization. Results show that the Hybrid algorithm has better or somewhat similar performance as compared to other algorithms that are based on population. The algorithm is successfully implemented on MATLAB. Keywords image segmentation; improved bee colony optimization; Tabu search; non-dominated sorted particle swarm optimization; non-dominated sorted genetic algorithm. 1 Introduction Image segmentation is a recent research topic from the last two decades that results in various techniques related to image segmentation. There exist several applications and problems domain are there that are required to be processes so that image data can be interpreted in a particular domain or application specific manner. However, problem domain depends on various image types that can be analysed and further processed like color, grayscale, range, infrared, sonar, X-ray and many more (Kumar et al., 2014). Image segmentation is partitioning of an image into segments on the basis of homogeneous features, similarity between pixels in a specified region depends on different criteria like color, intensity or texture, so that objects in an image can be identified based on variations of intensity in an image. Existing research has shown that human vision can identify the same texture that has gradient variations of intensity and many image segmentation methods are proposed based on the variation in intensity. Image segmentation has been used for several applications such as in machine vision applications, it is viewed as a bridge between low level and high level vision subsystems,