An Efficient Approach toward Color Image Segmentation with Combined Effort of Soft Clustering and Region Based Techniques using L Channel of LAB Color Space

an efficient approach for color image segmentation is always sought by the researchers in the color image processing research. We have different clustering based and region based methods for the same. But still there arises the requirement of an optimal method. In this paper, a new approach for color image segmentation is proposed. Here the segmentation is carried out on the L channel of LAB color space. The input color image is first converted from RGB to LAB. Then L channel is extracted from the LAB converted image and sent as input to FCM algorithm. After this initial segmentation, the segmented image is filtered by sobel filter. The filtered image is then segmented by Meyer's Watershed algorithm to produce the final segmented image of the original image. The results of the proposed approach are found efficient when the same are analyzed in terms of MSE and PSNR. Also the segmented images are found free from over segmentation.

[1]  Neeru Singla,et al.  Color image segmentation using k-means clustering algorithm , 2011 .

[2]  Tarun Gulati,et al.  SEGMENTING DIGITAL IMAGES USING EDGE DETECTION , 2013 .

[3]  Hoel Le Capitaine,et al.  A fast fuzzy c-means algorithm for color image segmentation , 2011, EUSFLAT Conf..

[4]  R. Maini.  Study and Comparison of Various Image Edge Detection Techniques , 2004 .

[5]  Salem Saleh Al-amri.  IMAGE SEGMENTATION BY USING EDGE DETECTION , 2010 .

[6]  R. Hunter.  Photoelectric Color Difference Meter , 1958 .

[7]  Anil Kumar Gupta,et al.  A Novel Approach Towards Clustering Based Image Segmentation , 2015, ArXiv.

[8]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[9]  Anil Kumar Gupta,et al.  Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation , 2015, ArXiv.

[10]  Anil Kumar Gupta,et al.  Impact of Exponent Parameter Value for the Partition Matrix on the Performance of Fuzzy C Means Algorithm , 2014, ArXiv.

[11]  Humera Tariq,et al.  K-Means Cluster Analysis for Image Segmentation , 2014 .

[12]  Fernand Meyer,et al.  Topographic distance and watershed lines , 1994, Signal Process..

[13]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[14]  Anil Kumar Gupta,et al.  Clustering Approach Towards Image Segmentation: An Analytical Study , 2014, ArXiv.

[15]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[16]  Md Rafiqul Islam,et al.  Segmentation of color image using adaptive thresholding and masking with watershed algorithm , 2013, 2013 International Conference on Informatics, Electronics and Vision (ICIEV).

[17]  C Rafael,et al.  Fundamentals of digital image processing: a practical approach with examples in Matlab - Solomon, Chris, Breckon, Toby , 2011 Book | Core text , 2015 .

[18]  V. Kavitha,et al.  Color Image Segmentation using ERKFCM , 2012 .

[19]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[20]  ALID,et al.  IMAGE SEGMENTATION : A WATERSHED TRANSFORMATION ALGORITHM , 2009 .

[21]  Indah Soesanti,et al.  OPTIMIZED FUZZY LOGIC APPLICATION FOR MRI BRAIN IMAGES SEGMENTATION , 2011 .

[22]  Nidhi Chandrakar,et al.  Study and comparison of various image edge detection techniques , 2012 .

[23]  Juraj Horváth,et al.  Image Segmentation Using Fuzzy C-Means , 2005 .