Scopus: 3 cites, Google Scholar: cites
MMKK++ algorithm for clustering heterogeneous images into an unknown number of clusters
Papp, Dávid (Budapest University of Technology and Economics. Department of Telecommunications and Media Informatics)
Szűcs, Gábor (Budapest University of Technology and Economics. Department of Telecommunications and Media Informatics)

Data: 2017
Resum: In this paper we present an automatic clustering procedure with the main aim to predict the number of clusters of unknown, heterogeneous images. We used the Fisher-vector for mathematical representation of the images and these vectors were considered as input data points for the clustering algorithm. We implemented a novel variant of K-means, the kernel K-means++, furthermore the min-max kernel K-means plusplus (MMKK++) as clustering method. The proposed approach examines some candidate cluster numbers and determines the strength of the clustering to estimate how well the data fit into K clusters, as well as the law of large numbers was used in order to choose the optimal cluster size. We conducted experiments on four image sets to demonstrate the efficiency of our solution. The first two image sets are subsets of different popular collections; the third is their union; the fourth is the complete Caltech101 image set. The result showed that our approach was able to give a better estimation for the number of clusters than the competitor methods. Furthermore, we defined two new metrics for evaluation of predicting the appropriate cluster number, which are capable of measuring the goodness in a more sophisticated way, instead of binary evaluation.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Image clustering ; Kernel k-means ; Cluster number ; Fisher-vector
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 16 Núm. 3 (2017) , p. 30-45 (Regular Issue) , ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.1054

16 p, 1.3 MB

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