An Integrated Color Image Segmentation with Multi-class SVM followed by SRFCM
C Ramesh1, T. Venugopal2

1C Ramesh, Research Scholar, Department of Computer Science, Rayalaseema University, Kurnool (Andhra Pradesh), India.
2Dr. T. Venugopal, Professor, Department of CSE Computer Science, Rayalaseema University, Kurnool (Andhra Pradesh), India.
Manuscript received on 22 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1607-1610 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B11140882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1114.0882S819
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Abstract: In existing the segmentation of a color image is mostly depends on the features color , texture or on both color and texture but proposed method for color image segmentation is based on both color and texture with multi-class SVM (Support Vector Machine).For color feature extraction we used homogeneity model and for textural features we used PLD (Power Law Descriptor). With the help of SR-FCM (Soft Rough Fuzzy-C-Means) clustering. Membership functions based on the fuzzy set are facing the major problem of cluster overlapping. The rough set concepts can help us to get correct data from incomplete data, uncertainty of data. For defining the soft set theory there is no any requirement of parameterization tools. To get improved results of proposed algorithm the combination of aspects of fuzzy sets, rough sets as well as soft sets are used. The feature extraction for textural feature is done by using spatial domain which helps to reduce the run time complexity. Proposed method provides better performance which is compared with all the state of art techniques which is developed and analyzed using MATLAB.
Keywords: Homogeneity, Clustering, Fuzzy Sets, Soft Sets, Power Law Descriptor, Texture, Segmentation, Classification, Rough Sets, Multi Class SVM.
Scope of the Article: Image Security