Image Classification through integrated K- Means Algorithm
Balasubramanian Subbiah · 2012
Image Classification has a significant role in the field of medical diagnosis as well as mining analysis and is even used for cancer diagnosis in the recent years. Clustering analysis is a valuable and useful tool for image classification and object diagnosis. A variety of clustering algorithms are available and still this is a topic of interest in the image processing field. However, these clustering algorithms are confronted with difficulties in meeting the optimum quality requirements, automation and robustness requirements. In this paper, we propose two clustering algorithm combinations with integration of K-Means algorithm that can tackle some of these problems. Comparison study is made between these two novel combination algorithms. The experimental results demonstrate that the proposed algorithms are very effective in producing desired clusters of the given data sets as well as diagnosis. These algorithms are very much useful for image classification as well as extraction of objects.