The batch clustering algorithm for classification application requires the initial parameters and also has a drifting phenomenon stochastic process. are critical to con-verge partial optimum. in original still space be improved thus speed up convergence based on parameters. This paper proposes an unsupervised method by addressing these two issues. Firstly, estimation been given preliminary with...

Title of dissertation: Semi-supervised and Active Image Clustering with Pairwise Constraints from Humans Arijit Biswas, Doctor of Philosophy, 2014 Dissertation directed by: Prof. David W. Jacobs Department of Computer Science University of Maryland, College Park Clustering images has been an interesting problem for computer vision and machine learning researchers for many years. However as the ...

In this paper, we present a clustering-based color indexing scheme for effective amd efficient image retrieval, which is essentially an exploration on the application of clustering technique to image retrieval. In our approach, the color features are clustered automatically using a color clustering algorithm twice (called two-level clustering processing) and two color feature summarizations are...

Image segmentation is the first step in image analysis and pattern recognition. Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. The accurate and effective algorithm for segmenting image is very useful in many fields, especially in medical image. This paper presents a new approach for image segmentation by applying k-means algor...

Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. This paper represents a frame work for clustering on image data. The objective of the frame w...

The objective of the present paper is to describe a pattern recognition approach for image segmentation using fuzzy clustering. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. Clustering analysis is one of the major techniques in pattern recognition. These fuzzy clustering algorithms have been widely studie...

This paper presents the first participation of the University of Ottawa group in the Photo Retrieval task at Image CLEF 2008. Our system uses Lucene for text indexing and LIRE for image indexing. We experiment with several clustering methods in order to retrieve images from diverse clusters. The clustering methods are: k-means clustering, hierarchical clustering, and our own method based on Wor...

Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. This paper represents a Genetic Algorithm for clustering on image data. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for ...

This paper presents the first participation of the University of Ottawa group in the Photo Retrieval task at Image CLEF 2008. Our system uses the following components: Lucene for text indexing and LIRE for image indexing. We experiment with several clustering methods in order to retrieve images from diverse clusters. The clustering methods are: k-means clustering, hierarchical clustering, and o...