A greedy strategy for images segmentation by support vector machines
Chih‐Hung Wu, Chun-Yen Chen, Yan-He Chen, Chialin Wu, Shing‐Hwang Doong · 2010
Image segmentation can be viewed as an essential step for extracting information from the images under investigation. Among many developed segmentation methods, the technique of clustering has been extensively studied. However, determining the number of clusters of an image is inherently a difficult problem, especially when a priori information on the image is unavailable. This study proposes a support vector machine approach for clustering images. To help determine the number of clusters, a greedy strategy is designed which extends or condenses the number of clusters by evaluating the clustering results from support vector machines. Comparisons on the effectiveness of the proposed method on various parameters settings are conducted. Experimental results are provided to illustrate the feasibility of the proposed approach.