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 suitable to represent local and global properties of images, and similarity measures that can be represented an affinity between these images. Second, we consider a clustering method for image collection. Here, we first build a coarser clustering by partitioning various images into several clusters using the flexible Mean shift algorithm and K-mean clustering 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 experiments 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 automatically 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 clustering method for large photographs collections using time and content features. First, we think about various types of feature vectors that are suitable to represent time and content of photographs, and we computed the similarity measures that can be represented an affinity between these photos. Next, we consider a clustering method for photo collection. Here, we first build a coarser clustering by automatically partitioning a given photo collection into several clusters using the Mean shift clustering algorithm. Second, we construct dense clustering by optimizing a Gaussian Dirichlet process mixture model taking initial clusters model as coarser clustering result. Finally, we have conducted the experiment which is able to evaluate a performance of our clustering method for various events photos collection. Experimental results show that both three types of features and Gaussian Dirichlet process mixture model brings about higher values of accuracy and precision in the clustering of photo-collection.

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