Semantic Fuzzy Image Segmentation Using Human Interaction

The aim of this paper is presentation of a new fuzzy image segmentation algorithm. In the proposed algorithm, human knowledge is used in clustering features for fuzzy image segmentation. In fuzzy clustering, the membership values of extracted features for each pixel at each cluster change proportional to zonal mean of membership values and gradient mean of adjacent pixels. The direction of membership variations are specified using human interaction. The proposed segmentation approach is applied for segmentation of texture and documentation images. Results show that the human interaction eventuates to clarification of texture and reduction of noise in segmented images.