Unsupervised Approach for Extracting the Textural Region of Interest from Real Image

Abstract

Neural network is an important technique in many image understanding areas. Then the performance of neural network depends on the separative degree among the input vector extracted from an original image. However, most methods are not enough to understand the contents of a image. Accordingly, we propose a efficient method of extracting a spatial feature from a real image, and segmenting the TROI (: Textural Region Of Interest) from the clustered image without a pre-knowledge. Our approach presents the 2-passing k-means algorithm for extracting a spatial feature from image, and uses the unsupervised learning scheme for the block-based image clustering. Also, a segmentation of the clustered TROI is achieved by tuning 2D Gabor filter to the spatial frequency the clustered region. In order to evaluate the performance of the proposed method, the segmenting quality was measured according to the goodness based on the segmented shape of region. Our experimental results showed that the performance of the proposed method is very successful.