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标题：Terrian Identification Using Co-Clustered Model of the Swarm Intellegence & Segmentation Technique
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作者：Ritesh Srivastava ; Shivani Agarwal ; Ankit Goel 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2012
卷号：2
期号：1
页码：239-249
DOI：10.5121/csit.2012.2122
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：A digital image is nothing more than data -- numbers indicating variations of red, green, and blue at a particular location on a grid of pixels. Clustering is the process of assigning data objects into a set of disjoint groups called clusters so that objects in each cluster are more similar to each other than objects from different clusters. Clustering techniques are applied in many application areas such as pattern recognition, data mining, machine learning, etc. Clustering algorithms can be broadly classified as Hard, Fuzzy, Possibility, and Probabilistic .K-means is one of the most popular hard clustering algorithms which partitions data objects into k clusters where the number of clusters, k, is decided in advance according to application purposes. This model is inappropriate for real data sets in which there are no definite boundaries between the clusters. After the fuzzy theory introduced by Lotfi Zadeh, the researchers put the fuzzy theory into clustering. Fuzzy algorithms can assign data object partially to multiple clusters. The degree of membership in the fuzzy clusters depends on the closeness of the data object to the cluster centers. The most popular fuzzy clustering algorithm is fuzzy c-means (FCM) which introduced by Bezdek in 1974 and now it is widely used. Fuzzy c-means clustering is an effective algorithm, but the random selection in center points makes iterative process falling into the local optimal solution easily. For solving this problem, recently evolutionary algorithms such as genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO) , and particle swarm optimization (PSO) have been successfully applied.
关键词：Image Segmentation; medical imaging; super pixels; Particle Swarm Optimization; FCM; Swarm ;Intellegence.