Automatic Classification for Various Images Collections Using Two Stages Clustering Method
Affiliation(s)
Department of Statistics, Chonnam National University, Gwagnju, Korea. School of School of Electronic & Computer Engineering, Chonnam National University, Gwangju, Korea.
Department of Statistics, Chonnam National University, Gwagnju, Korea. School of School of Electronic & Computer Engineering, Chonnam National University, Gwangju, Korea.
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.
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.
Cite this paper
W. Cho, I. Na, J. Choi and T. Lee, "Automatic Classification for Various Images Collections Using Two Stages Clustering Method," Open Journal of Applied Sciences, Vol. 3 No. 1, 2013, pp. 47-52. doi: 10.4236/ojapps.2013.31B010.
W. Cho, I. Na, J. Choi and T. Lee, "Automatic Classification for Various Images Collections Using Two Stages Clustering Method," Open Journal of Applied Sciences, Vol. 3 No. 1, 2013, pp. 47-52. doi: 10.4236/ojapps.2013.31B010.
References
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