Color and texture image segmentation

Abstract

For applications, such as image recognition or scene understanding, we cannot process the whole image directly for the reason that it is inefficient and unpractical. Therefore, to reduce the complexity of the recognition of the image, segmentation is a necessary step. Image segmentation divides an image into several parts (regions) according to some local homogeneous features of the image. For this purpose, understanding of the features of the image is important. Features such as color, texture, and patterns are considered for segmentation. Therefore, the thrust of our work is on the extraction of color textural features from images. Color measurement is done in Gaussian color space and texture features are extracted with Gabor filters. The paper proposes image segmentation based on recursive splitting k-means method and experiments are focused on color natural images taken from Berkeley Segmentation Dataset (BSD). © 2012 Springer-Verlag.