Abstract
Applying image retrieval techniques to large image databases requires the restriction of search space to provide adequate response time. This restriction can be done by means of clustering techniques to partition the image data set into subspaces of similar elements. In this article several clustering methods and validity indices are examined with regard to image categorization. A subset of the COIL-100 image collection is clustered by different agglomerative hierarchical methods as well as the k-Means, PAM and CLARA clustering algorithms. The validity of the resulting clusters is determined by computing the Davies-Bouldin-Index and Calinski-Harabasz-Index. To evaluate the performance of the different combinations of clustering methods and validity indices with regard to semantically meaningful clusters, the results are compared with a given reference grouping by measuring the Rand-Index.
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References
Calinski, R., Harabasz, J.: A Dendrite Method for Cluster Analysis. Communications in Statistics 3, 1–27 (1974)
Chen, J.-Y., Bouman, C.A., Dalton, J.C.: Hierarchical Browsing and Search of Large Image Databases. IEEE Transactions on Image Processing 9, 442–455 (2000)
Davies, D., Bouldin, D.: A Cluster Separation Measure. IEEE Transactions on PAMI 1, 224–227 (1979)
Hartigan, J.A., Wong, M.A.: A K-Means Clustering Algorithm. Applied Statistics 28, 100–108 (1979)
Hubert, L., Arabie, P.: Comparing Partitions. Journal of Classification 2, 193–218 (1985)
Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall Inc., Englewood Cliffs (1988)
Kämpfe, T., Käster, T., Pfeiffer, M., Ritter, H., Sagerer, G.: INDI – Intelligent Database Navigation by Interactive and Intuitive Content-Based Image Retrieval. In: IEEE ICIP, Rochester, USA, September 2002, vol. III, pp. 921–924 (2002)
Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, NewYork (1990)
Krishnamachari, S., Abdel-Mottaleb, M.: Hierachical Clustering Algorithms for Fast Image Retrieval. In: IS&T/SPIE Conference on Storage and Retrieval for Image andVideo Databases VII, San Jose, California, January 1999, pp. 427–435 (1999)
Maulik, U., Bandyopadhyay, S.: Performance Evaluation of Some Clustering Algorithms and Validity Indices. IEEE Transactions on PAMI 24(12), 1650–1654 (2002)
Milligan, G., Cooper, M.: An Examination of Procedures for Determining the Number of Clusters in a Dataset. Psychometrika 50(2), 159–179 (1985)
Nene, S., Nayar, S., Murase, H.: Columbia Object Image Library (COIL-100). Technical report, Department of Computer Science, Columbia University (February 1996)
Stricker, M.A., Orengo, M.: Similarity of Color Images. In: Storage and Retrieval for Image and Video Databases (SPIE), vol. III, pp. 381–392 (1995)
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Käster, T., Wendt, V., Sagerer, G. (2003). Comparing Clustering Methods for Database Categorization in Image Retrieval. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_30
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DOI: https://doi.org/10.1007/978-3-540-45243-0_30
Publisher Name: Springer, Berlin, Heidelberg
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