Automatic Classification for Various Images Collections Using Two Stages Clustering Method

Wan Hyun Cho, In Seop Na, Jun Yong Choi, Tae Hoon Lee

Department of Statistics, Chonnam National University, Gwagnju, Korea.
School of School of Electronic & Computer Engineering, Chonnam National University, Gwangju, Korea.

DOI: 10.4236/ojapps.2013.31B010                

Abstract

In this paper, we propose an automatic classification for various images collections using two stage clustering method. Here, we have used global and local image features. First, we review about various types of feature vector that is suita-ble to represent local and global properties of images, and similarity measures that can be represented an affinity be-tween these images. Second, we consider a clustering method for image collection. Here, we first build a coarser clus-tering by partitioning various images into several clusters using the flexible Mean shift algorithm and K-mean cluster-ing algorithm. Second, we construct dense clustering of images collection by optimizing a Gaussian Dirichlet process mixture model taking initial clusters as given coarser clustering. Finally, we have conducted the comparative experi-ments between our method and existing methods on various images datasets. Our approach has significant advantage over existing techniques. Besides integrating temporal and image content information, our approach can cluster auto-matically photographs without some assumption about number of clusters or requiring a priori information about initial clusters and it can also generalize better to different image collections.

Keywords

Automatic Classification; Images Collections; Clustering; Mean Shift; Gaussian Dirichlet Processing Mixture Model

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	K. Barnard, P. Duygulu, and D. Forsyth, Clustering art, Present in Comput. Vis. Pattern Recognition, Dec., 2001, pp 434-441.
[2]	J. Huang, S. R. Kumar, and R. Zabith, An automatic hierarchical image classification scheme, in ACM Conf. Multimedia, Set., 1998, pp 219-228.
[3]	C. Carson, S. Belongie, H. Greenspan, and J. Malik, Blobworld: Image segmentation using Ex-pectation-Maximization and Its Application to Image Querying, IEEE Tran. On PAMI, Vol. 24, No. 8, pp 1026-1038, 2002.
[4]	Y. Yang, D. Xu, F. Nie, S. Yan, and Y. Zhung, Image Clustering Using Local Discrimi-nant Models and Global Integration, IEEE Tran. On Im-age Processing, Vol. 19, No. 10, pp 2761-2773, 2010.
[5]	A. Sleit, A. Abugalhoun, M. Qatawneh, M.Ai-Sharief, R. Al-Jabaly and Ola Karajeh, Image clustering using Color, Texture and Shape features, KSII Tran. On Inter. And Infor. Syst., Vol. 5, No. 1, pp 212-227, 2011.
[6]	J. Goldberger, S. Gorden, and H. Greenspan, Unsupervided Image-Set Clustering Using an Information Theoretic Framework, IEEE Transaction on Image Processing, Vol. 15, 2006, pp 449-458.
[7]	S. Krinidis, M. Krinidis, and V. Chatzis, An Unsupervised Image Clustering Method Based on EEMD Image Histo-gram, Jour. Of Infor. Hiding and Multi. Sig. Processing, Vol. 3, No. 2, pp 152-163, 2012.
[8]	N. Sharma. S, P. Rawat. S and J. Singh. S, Efficient CBIR using Color Histogram Processing, Signal & Image Processing : An International Journal, Vol. 2, No.1, pp 94-112, 20011.
[9]	K. H. Ghazali, M. F. Mansor, M. M. Mus-tafa and A. Hussain, Feature Extraction Technique using Discrete Wavelet Transform for Image Classification, The 5th Student conference on Research and Develop-ment, 11-12 December 2007, Malaysia.
[10]	R. J. Lo-pez-Sastre, T. Tuytelaars, F. J. Acevedo-Rodriguez, and S. Maldonado-Bascon, Towards a more discriminative and semantic visual vocabulary. Computer Vsion and Image Understanding, Vol. 115, pp 415-425, 2011.
[11]	T. Deselaers, L. Pimenidis, and H. Ney, Bag-of-Visual Words Models for Adult Image Classification and Fil-tering, 208 IEEE
[12]	C. Xiao and M. Liu, Efficient Mean-shift Clustering Gaussian KD-Tree, Pacific Graphics, Vol. 9, No. 7, pp , 2010
[13]	D. M. Blei and M. I. Jordan, "Variational Inference for Dirichlet Process Mixtures," Bayesian Analysis, vol. 1, no. 1 pp 121-144, 2006.