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On each scale, the improved objective function is optimized by taking advantage of Lagrange multipliers, and the final label of wavelet coefficient is determined by iteratively updating the membership degree and cluster centers. The experimental results on synthetic images, natural scenery color images and remote sensed images show that the proposed algorithm obtains much better segmentation results, such as accurately differentiating different regions and being immune to noise.<\/jats:p>","DOI":"10.1142\/s0218001413550057","type":"journal-article","created":{"date-parts":[[2013,4,15]],"date-time":"2013-04-15T03:51:21Z","timestamp":1365997881000},"page":"1355005","source":"Crossref","is-referenced-by-count":4,"title":["FUZZY CLUSTERING ALGORITHM FOR INTEGRATING MULTISCALE SPATIAL CONTEXT IN IMAGE SEGMENTATION BY HIDDEN MARKOV RANDOM FIELD MODELS"],"prefix":"10.1142","volume":"27","author":[{"given":"GUO-YING","family":"LIU","sequence":"first","affiliation":[{"name":"Department of Computer and Engineering, Anyang Normal University, Xiange Road Anyang, Henan 455002, P. R. 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