Shape and texture clustering: Best estimate for the clusters number

Authors:
Mohammad Reza Daliri;Vincent Torre
Affiliations:
International School for Advanced Studies, Area Science Park, SS 14 Km 163.5, Edificio Q, 34012 Basovizza (TS), Italy and ICTP Programme for Training and Research in Italian Laboratories, Internat ...;International School for Advanced Studies, Area Science Park, SS 14 Km 163.5, Edificio Q, 34012 Basovizza (TS), Italy
Venue:
Image and Vision Computing
Year:
2009

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Abstract

The most difficult problem in automatic clustering is the determination of the total number of final clusters N"c"l"u"s"t"e"r. In the present paper, a new method for finding N"c"l"u"s"t"e"r is proposed and is compared with previously developed methods. The proposed method is based on the minimization of the functional @q(N"c"l"u"s"t"e"r)=@aN"c"l"u"s"t"e"r+@b@?iN"c"l"u"s"t"e"r1n"i+1N"c"l"u"s"t"e"r@?i=1N"c"l"u"s"t"e"rdist(C"i) where n"i is the number of shapes and textures in cluster C"i, dist(C"i) is the intra-cluster distance and @a and @b are two parameters controlling the grain of the clustering. The proposed method provides almost perfect clustering for the Kimia-25, Kimia-99, MPEG-7 shape databases, subset of Brodatz, full Brodatz and UIUCTex texture databases and provides better results than all previously proposed methods for automatic clustering.