Object Extraction Using Topological Models from Complex Scene Image

Efficient and effective object recognition from a multimedia data are very complex. Automatic object segmentation is usually very hard for natural images; interactive schemes with a few simple markers provide feasible solutions. In this chapter, we propose topological model based region merging. In this work, we will focus on topological models like, Relative Neighbourhood Graph (RNG) and Gabriel graph (GG), etc. From the Initial segmented image, we constructed a neighbourhood graph represented different regions as the node of graph and weight of the edges are the value of dissimilarity measures function for their colour histogram vectors. A method of similarity based region merging mechanism (supervised and unsupervised) is proposed to guide the merging process with the help of markers. The region merging process is adaptive to the image content and it does not need to set the similarity threshold in advance. To the validation of proposed method extensive experiments are performed and the result shows that the proposed method extracts the object contour from the complex background.

[1]  Faruq A. Al-Omari,et al.  Fast Video Shot Boundary Detection Technique based on Stochastic Model , 2016, Int. J. Comput. Vis. Image Process..

[2]  Frank Nielsen,et al.  Statistical region merging , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[4]  Olga Veksler,et al.  Star Shape Prior for Graph-Cut Image Segmentation , 2008, ECCV.

[5]  Robin Sibson,et al.  Locally Equiangular Triangulations , 1978, Comput. J..

[6]  Frédo Durand,et al.  A Topological Approach to Hierarchical Segmentation using Mean Shift , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  David Zhang,et al.  Automatic Image Segmentation by Dynamic Region Merging , 2010, IEEE Transactions on Image Processing.

[9]  L. Joshua Leon,et al.  Watershed-Based Segmentation and Region Merging , 2000, Comput. Vis. Image Underst..

[10]  Wei-Ying Ma,et al.  Learning similarity measure for natural image retrieval with relevance feedback , 2002, IEEE Trans. Neural Networks.

[11]  Kenneth E. Barner,et al.  Joint region merging criteria for watershed-based image segmentation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[12]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

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

[15]  Alfred Mertins,et al.  Scalable multiresolution color image segmentation , 2006, Signal Process..

[16]  Joachim M. Buhmann,et al.  Empirical Evaluation of Dissimilarity Measures for Color and Texture , 2001, Comput. Vis. Image Underst..

[17]  Hichem Sahli,et al.  Multiscale gradient watersheds of color images , 2003, IEEE Trans. Image Process..

[18]  Lei Zhang,et al.  Canny edge detection enhancement by scale multiplication , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[20]  Simone Santini,et al.  Similarity Measures , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Charles T. Zahn,et al.  Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters , 1971, IEEE Transactions on Computers.

[22]  Heng-Da Cheng,et al.  Color image segmentation based on homogram thresholding and region merging , 2002, Pattern Recognit..

[23]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[24]  Max Mignotte,et al.  Segmentation by Fusion of Histogram-Based $K$-Means Clusters in Different Color Spaces , 2008, IEEE Transactions on Image Processing.

[25]  John F. O'Callaghan,et al.  An Alternative Definition for "Neighborhood of a Point" , 1975, IEEE Transactions on Computers.

[26]  Yangyang Li,et al.  Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation , 2015, Inf. Sci..

[27]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[29]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[30]  Eros Comunello,et al.  Learning a nonlinear distance metric for supervised region-merging image segmentation , 2011, Comput. Vis. Image Underst..

[31]  Jian Yang,et al.  Image segmentation by iterated region merging with localized graph cuts , 2011, Pattern Recognit..

[32]  Philip M. Lankford.  Regionalization: Theory and Alternative Algorithms , 2010 .

[33]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[34]  Muhammad Hussain,et al.  Ensemble Classifier for Benign-Malignant Mass Classification , 2013, Int. J. Comput. Vis. Image Process..

[35]  Xiaochun Yang,et al.  Image segmentation with a fuzzy clustering algorithm based on Ant-Tree , 2008, Signal Process..

[36]  Jian Sun,et al.  Lazy snapping , 2004, SIGGRAPH 2004.