A Study of Automatic Color Image Segmentation Technology Based on Spatial Information

• Main Authors: Zi-Chun Chen, 陳姿君
• Other Authors: Hsien-Chu Wu
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/82e89v

碩士 === 臺中技術學院 === 資訊科技與應用研究所 === 98 === Since a color image usually contains a number of various objects, the main purpose of the color image segmentation is to clearly divide a color image into several objects or clusters where each object contains pixels that are similar or deemed to belong to the same group. Through the segmentation technique, one can extract objects that are interested by users or preprocess images for other operations such as object recognition and image retrieval. In this thesis, the first technique presents a simple and fast method to segment the color images. The method begins by extracting individual R, G and B color channels, and then the method proceeds to form histons by analyzing the frequency changes in each color channels. An image histon is the histogram formed by calculating the relationships between individual pixel value and that pixel’s neighboring pixels. With the frequency changes of histon in each channel, the method determines the thresholds and uses these values as segment points in each channel. Then, the method permutes segmenting points to generate all possible combinations of three segmenting points, where each point is selected from R, G and B color channel respectively. Every single set of R, G and B values will be treated as a new frequency point. These new frequency points form a new RGB histon. The final threshold values can be found by analyzing the frequency changes in this new RGB histon. The proposed method can segment objects in color images effectively. The second technique in this thesis presents an efficient algorithm to obtain the segmented images for color image segmentation. The proposed method employs the information of color histon and applies to the improved fuzzy c-means algorithm. A histon is compiled based on color information in RGB color space and spatial information among the central pixel and its neighbors. The proposed method first obtains new bins by quantizing each of RGB channels. The purpose of this procedure is to find representative colors of the image. The algorithm proceeds to calculate the similarity between the central pixel and its neighborhoods and build a histon in order to obtain the spatial information. As bins and the histon have been obtained, calculate the difference of the ith bin and i+1st bin in histon to set the initial number of clusters for the image. Finally, apply the processed spatial information to the improved fuzzy c-means algorithm for image segmentation.