Abstract
Case-based object recognition requires a general case of the object that should be detected. Real world applications such as the recognition of biological objects in images cannot be solved by one general case. A case-base is necessary to handle the great natural variations in the appearance of these objects. In this paper we will present how to learn a hierarchical case base of general cases. We present our conceptual clustering algorithm to learn groups of similar cases from a set of acquired structural cases. Due to its concept description it explicitly supplies for each cluster a generalized case and a measure for the degree of its generalization. The resulting hierarchical case base is used for applications in the field of case-based object recognition.
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References
Perner, P.: Data Mining on Multimedia Data. Springer, Berlin (1998)
Mucha, H.J.: Clusteranalyse mit Mikrocomputern. Akademie Verlag, Berlin (1992)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)
Rasmussen, E.: Clustering Algorithms. In: Frakes, W.B., Baeza-Yates, R. (eds.) Information Retrieval, pp. 419–442. Prentice Hall, Englewood Cliffs (1992)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)
Gupta, S.K., Rao, K.S., Bhatnagar, V.: K-means Clustering Algorithm for Categorical Attributes. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 203–208. Springer, Heidelberg (1999)
Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. J. Cybern. 4, 95–104 (1974)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2), 224–227 (1979)
Fisher, D., Langley, P.: Approaches to conceptual clustering. In: Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, pp. 691–697 (1985)
Perner, P.: Different Learning Strategies in a Case-Based Reasoning System for Image Interpretation. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 251–261. Springer, Heidelberg (1998)
Iba, W., Langley, P.: Unsupervised Learning of Probabilistic Concept Hierarchies. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049. Springer, Heidelberg (2001)
Perner, P., Jänichen, S.: Case Acquisition and Case Mining for Case-Based Object Recognition. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 616–629. Springer, Heidelberg (2004)
Lance, G.N., Williams, W.T.: A General Theory of Classification Sorting Strategies, 1. Hierarchical Systems. Comp. J. 9, 373–380 (1966)
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Jänichen, S., Perner, P. (2005). Acquisition of Concept Descriptions by Conceptual Clustering. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_16
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DOI: https://doi.org/10.1007/11510888_16
Publisher Name: Springer, Berlin, Heidelberg
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