Abstract：An improved mean shift segmentation approach for natural images is presented. And its application to object detection in viewbased navigation is proposed. In the improved segmentation, the color bandwidth derived from data analysis for clustering is estimated according to the image data. The density of each pixel is calculated based on the estimated bandwidth, and the local modes are searched by the direct density search way. Then a global standard for local mode merging is applied to get the final segmentation result. In its application to object detection, the object models constructed offline are used to verify the possible objects among the segmented regions. The experimental results indicate that the propesed approach can effectively detect the natural objects under complex backgrounds.
洪义平，易建强，赵冬斌，李新征. 自然图像分割方法及其在目标检测中的应用[J]. 模式识别与人工智能, 2006, 19(5): 658-662.
HONG YiPing, YI JianQiang, ZHAO DongBin, LI XinZheng. Segmentation Approach for Natural Images and Application to Object Detection in ViewBased Navigation. , 2006, 19(5): 658-662.
[1] Zhu S, Lee T, Yuille A. Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 1996, 18(9): 884-900 [2] Tang Ming, Ma Songde. Non-Parametric Region Competition: A New Scheme for Image Segmentation. Acta Automatica Sinica, 2001, 27(6): 737-743 (in Chinese) (唐 明,马颂德.非参数化区域竞争方法:一种新的图像分割框架.自动化学报, 2001, 27(6): 737-743) [3] Tu Z W, Zhu S C. Image Segmentation by Data-Driven Markov Chain Monte Carlo. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(5): 657-673 [4] Fukunaga K, Hostetler L D. The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Trans on Information Theory, 1975, 21(1): 32-40 [5] Comaniciu D, Meer P. Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619 [6] Comaniciu D, Meer P. An Algorithm for Data-Driven Bandwidth Selection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(2): 281-288 [7] Cheng Y. Mean Shift, Mode Seeking, and Clustering. IEEE Trans on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799 [8] Malik J, Belongie S, Leung T, et al. Contour and Texture Analysis for Image Segmentation. International Journal of Computer Vision, 2001, 43(1): 7-27 [9] Shi J B, Malik J. Normalized Cuts and Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905 [10] Martin D, Fowlkes C, Malik J. Learning to Detect Natural Image Boundaries Using Local Brightness, Color and Texture Cues. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(5): 530-549 [11] Bennamoun M, Mamic G. Object Recognition. London, UK: Springer, 2002 [12] Hrdle W, Müller M, Sperlich S, et al. Nonparametric and Semiparametric Models. London, UK: Springer, 2004 [13] Silverman B W. Density Estimation for Statistics and Data Analysis. London, UK: Chapman and Hall, 1986 [14] Terrillon J C, Shirazi M N, Fukamachi H, et al. Comparative Performance of Different Skin Chrominance Models and Chrominance Spaces for the Automatic Detection of Human Faces in Color Images // Proc of the 4th IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble, France, 2000: 54-61
2006, Vol. 19 
