Implementation on Feature Selection for Image Segmentation

In case of image analysis, image processing one of the crucial steps is segmentation of image. Segmentation of image concerns about dividing entire image in sub parts that may be similar or dissimilar with respect to features. Output of image segmentation has consequence on analysis of image, further processing of image. Analysis of image comprises depiction of object and object representation, measurement of feature. Therefore characterization, area of interests visualization in the image, description have crucial job in segmentation of image. Most image segmentation algorithms optimize some mathematical similarity criterion derived from several low-level image features. One possible way of combining different types of features, e. g. color- and texture features on different scales and/or different orientations, is to simply stack all the individual measurements into one high-dimensional feature vector. Due to the nature of such stacked vectors, however, only very few components (e. g. those which are defined on a suitable scale) will carry information that is relevant for the actual segmentation task. We present an approach to combining segmentation and feature selection that overcomes this relevance determination problem. All free model parameters of this method are selected by a resampling-based stability analysis. Experiments demonstrate that the built-in feature selection mechanism leads to stable and meaningful partitions of the images. This survey explains some methods of image segmentation.