TY - GEN
T1 - A novel approach for determination of optimal number of cluster
AU - Ganguly, Debashis
AU - Mukherjee, Swarnendu
AU - Naskar, Somnath
AU - Mukherjee, Partha
PY - 2009/6/8
Y1 - 2009/6/8
N2 - Image clustering and categorization is a means for high-level description of image content. In the field of contentbased image retrieval (CBIR), the analysis of gray scale images has got very much importance because of its immense application starting from satellite images to medical images. But the analysis of an image with such number of gray shades becomes very complex, so, for simplicity we cluster the image into a lesser number of gray levels. Using K-Means clustering algorithm we can cluster an image to obtain segments. The main disadvantage of the k-means algorithm is that the number of clusters, K, must be supplied as a parameter. Again, this method does not specify the optimal cluster number. In this paper, we have provided a mathematical approach to determine the optimal cluster number of a clustered grayscale images. A simple index, based on the intra-cluster and inter-cluster distance measures has been proposed in this paper, which allows the number of clusters to be determined automatically.
AB - Image clustering and categorization is a means for high-level description of image content. In the field of contentbased image retrieval (CBIR), the analysis of gray scale images has got very much importance because of its immense application starting from satellite images to medical images. But the analysis of an image with such number of gray shades becomes very complex, so, for simplicity we cluster the image into a lesser number of gray levels. Using K-Means clustering algorithm we can cluster an image to obtain segments. The main disadvantage of the k-means algorithm is that the number of clusters, K, must be supplied as a parameter. Again, this method does not specify the optimal cluster number. In this paper, we have provided a mathematical approach to determine the optimal cluster number of a clustered grayscale images. A simple index, based on the intra-cluster and inter-cluster distance measures has been proposed in this paper, which allows the number of clusters to be determined automatically.
UR - http://www.scopus.com/inward/record.url?scp=66249102968&partnerID=8YFLogxK
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U2 - 10.1109/ICCAE.2009.40
DO - 10.1109/ICCAE.2009.40
M3 - Conference contribution
AN - SCOPUS:66249102968
SN - 9780769535692
T3 - Proceedings - 2009 International Conference on Computer and Automation Engineering, ICCAE 2009
SP - 113
EP - 117
BT - Proceedings - 2009 International Conference on Computer and Automation Engineering, ICCAE 2009
T2 - 2009 International Conference on Computer and Automation Engineering, ICCAE 2009
Y2 - 8 March 2009 through 10 March 2009
ER -