A fuzzy inference model for image segmentation

Abstract

We present a novel method to segment objects in images based on the similarity measurement of fuzzy gray level technique in this paper. In our model, we classify the processing steps into three stages. First, we utilize the attributes of luminance and chromaticity components of HLS color coordinate system to form a fuzzy gray level. These attributes can describe the relationship between different frequent colors and the image can be transferred to smooth gray level, which can capture the objects in images. Second, we reduce the gray levels of image pixels to lower gray levels to speed up computation. Third, we label each root pixel based on a similarity measurement. We perform a sliding window to move from one block to the next one. The similarity of the two root pixels blocked by the sliding window depends on their neighboring pixels. Via the similarity computation, we assign a label number to the root pixels. We generate objects from grouping different labels. The image data are classified by fuzzy gray level technique and the objects are segmented from images. According to the simulation results, our model shows the efficiency and effectiveness for image segmentation.