Cluster-Based Image Segmentation Using Fuzzy Markov Random Field

  • سال انتشار: 1395
  • محل انتشار: دوفصلنامه مجله کامپیوتر و رباتیک، دوره: 9، شماره: 2
  • کد COI اختصاصی: JR_JCR-9-2_001
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 368
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نویسندگان

Peyman Rasouli

Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Mohammad Reza Meybodi

Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran

چکیده

Image segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. Markov random field (MRF) is a tool for modeling statistical and structural information at the same time. Fuzzy Markov random field (FMRF) is a MRF in fuzzy space which handles fuzziness and randomness of data simultaneously. This paper propose a new method called FMRFC which is model clustering using FMRF and applying it in application of image segmentation. Due to the similarity of FMRF model structure and image neighbourhood structure, exploiting FMRF in image segmentation makes results in acceptable levels. One of the important tools is Cellular learning automata (CLA) for suitable initial labelling of FMRF. The reason for choosing this tool is the similarity of CLA to FMRF and image structure. We compared the proposed method with several approaches such as Kmeans, FCM, and MRF and results demonstratably show the good performance of our method in terms of tanimoto, mean square error and energy minimization metrics.

کلیدواژه ها

Clustering, Image segmentation, Markov random field, Fuzzy markov random field, Cellular learning automata

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