Abstract：A method based on spatial multiscale analysis is put forward. Firstly, an image is divided into subdomains with different sizes by analyzing the energy projection distribution on rows and columns of the image, and the sizes are adjusted adaptively according to both holistic and local features. Then, wavelet transform to each subimage is adopted and the statistics which reflect the local features are extracted. Finally, an improved fuzzy cmeans (FCM) method is adopted to cluster the eigenvectors, thus the image segmentation is realized. Experimental results have proved that the proposed method can speed up operation as well as improve the segmentation results.
[1] Chai Yuhua, Gao Liqun, Lu Shun. WaveletBased Watershed for Image Segmentation Algorithm // Proc of the 6th World Congress on Intelligent Control and Automation. Dalian, China, 2006, Ⅱ: 95959599 [2] Sun J, Gu D, Zhang S, et al. Hidden Markov Bayesian Texture Segmentation Using Complex Wavelet Transform Vision. IEE Proc of Image and Signal Processing, 2004, 151(3): 215223 [3] Li Hong, Zheng Chengyong, Gao Jinli. Weak and Small Object Detection Based on Wavelet MultiScale Analysis and Fisher Algorithm. Journal of Infrared and Millimeter Waves, 2003, 22(5): 353356 (in Chinese) (李 红,郑成勇,高景丽.基于小波多尺度分析及Fisher分割的红外弱小目标检测.红外与毫米波学报, 2003, 22(5): 353356) [4] Wang Weihong, Zhao Xiaoming, Feng Xiancheng. Parallel WaveletBased Image Segmentation Using MPI // Proc of the IEEE Region 10 Annual International Conference (TENCON 2004). Chiang Mai, Thailand. 2004, Ⅱ: 9799 [5] Hodgson M E. What Size Window for Image Classification? A Cognitive Perspective. Photogrammetric Engineering and Remote Sensing, 1998, 64(8): 797807 [6] Myint S W, Lam N S N, Tyler J M. An Evaluation of Four Different Wavelet Decomposition Procedures for Spatial Feature Discrimination in Urban Areas. Transactions in GIS, 2002, 6(4): 403429 [7] Myint S W. Fractal Approaches in Texture Analysis and Classification of Remotely Sensed Data: Comparisons with Spatial Autocorrelation Techniques and Simple Descriptive Statistics. International Journal of Remote Sensing, 2003, 24(9): 19251947 [8] Huang Xin, Zhang Liangpei, Li Pingxiang. SSMC Method for the Classification of High Spatial Resolution Remotely Sensed Images. Journal of Image and Graphics, 2006, 11(4): 529534 (in Chinese) (黄 昕,张良赔,李平湘.高空间分辨率遥感图像分类的SSMC方法.中国图象图形学报, 2006, 11(4): 529534) [9] van der Schaaf A, van Hateren J H. Modeling the Power Spectra of Natural Images: Statistics and Information. Vision Research, 1996, 36(17): 27592770 [10] Bian Zhaoqi, Zhang Xuegong. Pattern Recognition. 2nd Edition. Beijing, China: Tsinghua University Press, 2000 (in Chinese) (边肇祺,张学工.模式识别.第2版.北京:清华大学出版社, 2000)
2008, Vol. 21 
