Digital Image Processing

K. S. LakshmiPriya ¹, L. Sankari ²

Affiliations
1 Sri Ramakrishna CAS for Women, Coimbatore-44, IN
2 Department of Computer Science, Sri Ramakrishna CAS for Women, Coimbatore-44, IN

Abstract

In Recent days Semi Supervised Clustering plays a noteworthy role in image processing which helps the image segmentation to produce the efficient result of an input image. Semi Supervised clustering which is combinational of both labeled and unlabeled data points, typically with the large amount of unlabeled data and a small amount of labeled data. Semi supervised clustering falls between unsupervised (without any labeled training data) and supervised (with completely labeled training data). It means that a small amount of human assistance or prior information is given during clustering process. This paper is focuses on survey of Semi Supervised clustering techniques for image segmentation.

Keywords

Semi Supervised Clustering, Labeled Data, Unlabeled Data.

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L. Sankari ¹, C. Chandrasekar ²

Affiliations
1 Department of comp.Science, Sri Ramakrishna College of Arts and Science for women, Coimbatore, IN
2 Dept of Computer Science, Periyar University, Salem, IN

Abstract

In image processing, segmentation means dividing the image into homogeneous regions. The task of recognizing the patterns is a crucial task. Since the result is based on which image segmentation algorithm the application is using. A number of image segmentation algorithms are available using data mining techniques like clustering based algorithms, classification based algorithms and semi supervised based algorithms. This paper discusses about two semi supervised image segmentation ideas with one standard model based algorithm (EM Cluster Algorithm). In semi supervised method both labeled and unlabeled data are used to improve the performance of segmentation. The first paper discuss about standard EM algorithms The second paper discuss about semi supervised image segmentation using mouse clicks as prior information and the third paper discuss about optimal seed selection with semi supervised segmentation. The result of analysis shows that the optimal seed selection (method III) gives better results and then clustering gives more accurate results. The Image attributes are intensity and color.

Keywords

Semi Supervised Clustering, Image Segmentation, Em Clustering, Model Based Clustering.

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