Seeded region growing: an extensive and comparative study

Seeded region growing (SRG) algorithm is very attractive for semantic image segmentation by involving high-level knowledge of image components in the seed selection procedure. However, the SRG algorithm also suffers from the problems of pixel sorting orders for labeling and automatic seed selection. An obvious way to improve the SRG algorithm is to provide more effective pixel labeling technique and automate the process of seed selection. To provide such a framework, we design an automatic SRG algorithm, along with a boundary-oriented parallel pixel labeling technique and an automatic seed selection method. Moreover, a seed tracking algorithm is proposed for automatic moving object extraction. The region seeds, which are located inside the temporal change mask, are selected for generating the regions of moving objects. Experimental evaluation shows good performances of our technique on a relatively large variety of images without the need of adjusting parameters.

[1]  Murat Kunt,et al.  Recent results in high-compression image coding (Invited Papaer) , 1987 .

[2]  B. S. Manjunath,et al.  Unsupervised Segmentation of Color-Texture Regions in Images and Video , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Jianping Fan,et al.  Adaptive motion-compensated video coding scheme towards content-based bit rate allocation , 2000, J. Electronic Imaging.

[4]  Aggelos K. Katsaggelos,et al.  Hybrid image segmentation using watersheds and fast region merging , 1998, IEEE Trans. Image Process..

[5]  Patrick Bouthemy,et al.  Motion segmentation and qualitative dynamic scene analysis from an image sequence , 1993, International Journal of Computer Vision.

[6]  King Ngi Ngan,et al.  Automatic segmentation of moving objects for video object plane generation , 1998, IEEE Trans. Circuits Syst. Video Technol..

[7]  A. Murat Tekalp,et al.  Guest editorial introduction to the special issue on image and video processing for digital libraries , 2000, IEEE Trans. Image Process..

[8]  Jianping Fan,et al.  Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..

[9]  Minoru Asada,et al.  MDL-Based Segmentation and Motion Modeling in a Long Image Sequence of Scene with Multiple Independently Moving Objects , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Josef Kittler,et al.  A Performance Measure for Boundary Detection Algorithms , 1996, Comput. Vis. Image Underst..

[13]  XINQUAN SHEN,et al.  Segmentation Of 2D And 3D Images Through A Hierarchical Clustering Based On Region Modelling , 1998, Pattern Recognit..

[14]  Georgios Tziritas,et al.  A semi-automatic seeded region growing algorithm for video object localization and tracking , 2001, Signal Process. Image Commun..

[15]  Nuggehally Sampath Jayant,et al.  An adaptive clustering algorithm for image segmentation , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[16]  Edward H. Adelson,et al.  Representing moving images with layers , 1994, IEEE Trans. Image Process..

[17]  Levent Onural,et al.  Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework , 1998, IEEE Trans. Circuits Syst. Video Technol..

[18]  H. D. Cheng,et al.  Thresholding using two-dimensional histogram and fuzzy entropy principle , 2000, IEEE Trans. Image Process..

[19]  Jianping Fan,et al.  Spatiotemporal segmentation for compact video representation , 2001, Signal Process. Image Commun..

[20]  Charles A. Bouman,et al.  A multiscale random field model for Bayesian image segmentation , 1994, IEEE Trans. Image Process..

[21]  Murat Kunt,et al.  Spatiotemporal Segmentation Based on Region Merging , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[23]  Ming-Chieh Lee,et al.  Semiautomatic segmentation and tracking of semantic video objects , 1998, IEEE Trans. Circuits Syst. Video Technol..

[24]  Jianping Fan,et al.  An automatic algorithm for semantic object generation and temporal tracking , 2002, Signal Process. Image Commun..

[25]  Xiaobo Li,et al.  Adaptive image region-growing , 1994, IEEE Trans. Image Process..

[26]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[27]  Jake K. Aggarwal,et al.  The Integration of Image Segmentation Maps using Region and Edge Information , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[29]  Alain Trémeau,et al.  Regions adjacency graph applied to color image segmentation , 2000, IEEE Trans. Image Process..

[30]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[31]  Sang Uk Lee,et al.  On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques , 1990, Pattern Recognit..

[32]  Norbert Diehl,et al.  Object-oriented motion estimation and segmentation in image sequences , 1991, Signal Process. Image Commun..

[33]  Theodosios Pavlidis,et al.  Integrating Region Growing and Edge Detection , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Andrew Mehnert,et al.  An improved seeded region growing algorithm , 1997, Pattern Recognit. Lett..

[35]  Josef Kittler,et al.  Region growing: a new approach , 1998, IEEE Trans. Image Process..

[36]  John F. Haddon,et al.  Image Segmentation by Unifying Region and Boundary Information , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..