Region segmentation techniques for object-based image compression: a review

Image compression based on transform coding appears to be approaching an asymptotic bit rate limit for application-specific distortion levels. However, a new compression technology, called object-based compression (OBC) promises improved rate-distortion performance at higher compression ratios. OBC involves segmentation of image regions, followed by efficient encoding of each region’s content and boundary. Advantages of OBC include efficient representation of commonly occurring textures and shapes in terms of pointers into a compact codebook of region contents and boundary primitives. This facilitates fast decompression via substitution, at the cost of codebook search in the compression step. Segmentation cose and error are significant disadvantages in current OBC implementations. Several innovative techniques have been developed for region segmentation, including (a) moment-based analysis, (b) texture representation in terms of a syntactic grammar, and (c) transform coding approaches such as wavelet based compression used in MPEG-7 or JPEG-2000. Region-based characterization with variance templates is better understood, but lacks the locality of wavelet representations. In practice, tradeoffs are made between representational fidelity, computational cost, and storage requirement. This paper overviews current techniques for automatic region segmentation and representation, especially those that employ wavelet classification and region growing techniques. Implementational discussion focuses on complexity measures and performance metrics such as segmentation error and computational cost.

[1]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

[2]  A. Ravishankar Rao,et al.  Identifying high-level features of texture perception , 1992, Electronic Imaging.

[3]  Xavier Cufí,et al.  Strategies for image segmentation combining region and boundary information , 2003, Pattern Recognit. Lett..

[4]  Michele Nappi,et al.  Linear prediction image coding using iterated function systems , 1999, Image Vis. Comput..

[5]  Terry Caelli,et al.  On the classification of image regions by colour, texture and shape , 1993, Pattern Recognit..

[6]  Errol J. Wood,et al.  Applying Fourier and Associated Transforms to Pattern Characterization in Textiles , 1990 .

[7]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[8]  Rae-Hong Park,et al.  Texture periodicity detection: features, properties, and comparisons , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[9]  Jorge S. Marques.  Periodicity estimation in textured images using to ML approach , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Peter Schelkens,et al.  A comparative study of scalable video coding schemes utilizing wavelet technology , 2004, SPIE Optics East.

[11]  Georgios S. Paschos,et al.  Fast color texture recognition using chromaticity moments , 2000, Pattern Recognit. Lett..

[12]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Shen Furao,et al.  A fast no search fractal image coding method , 2004, Signal Process. Image Commun..

[14]  Paul Scheunders,et al.  Wavelet correlation signatures for color texture characterization , 1999, Pattern Recognit..

[15]  Gerhard X. Ritter,et al.  Boundary representation techniques for object-based image compression , 2004, SPIE Optics + Photonics.

[16]  William A. Pearlman,et al.  Texture coding using a Wold decomposition model , 1996, IEEE Trans. Image Process..

[17]  Jerry M. Mendel,et al.  Semi-Markov Random Field Models For Texture Synthesis , 1987, Other Conferences.

[18]  Y. Fisher,et al.  Image compression: A study of the iterated transform method , 1992, Signal Process..

[19]  Gregory K. Wallace,et al.  Overview of the JPEG (ISO/CCITT) still image compression standard , 1990, Other Conferences.

[20]  Philippe Salembier,et al.  Overview of the MPEG-7 Standard and of Future Challenges for Visual Information Analysis , 2002, EURASIP J. Adv. Signal Process..

[21]  S. Zucker,et al.  Finding structure in Co-occurrence matrices for texture analysis , 1980 .

[22]  Bea Thai,et al.  Modeling and Classifying Symmetries Using a Multiscale Opponent Color Representation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Joachim M. Buhmann,et al.  On learning texture edge detectors , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[24]  Joachim M. Buhmann,et al.  Semi-supervised Image Segmentation by Parametric Distributional Clustering , 2003, EMMCVPR.

[25]  Thomas S. Huang,et al.  Automated region segmentation using attraction-based grouping in spatial-color-texture space , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[26]  Paul Smith,et al.  Layered motion segmentation and depth ordering by tracking edges , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Michael G. Strintzis,et al.  Video scene segmentation using spatial contours and 3-D robust motion estimation , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Joachim M. Buhmann,et al.  Coupled Clustering: A Method for Detecting Structural Correspondence , 2001, J. Mach. Learn. Res..

[30]  R L Somorjai,et al.  Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia. , 1997, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[31]  Ling-Hwei Chen,et al.  Unsupervised Texture Segmentation by Determining the Interior of Texture Regions Based on Wavelet Transform , 2001, Int. J. Pattern Recognit. Artif. Intell..

[32]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .

[33]  Ling-Hwei Chen,et al.  A New Method for Extracting Primitives of Regular Textures Based on Wavelet Transform , 2002, Int. J. Pattern Recognit. Artif. Intell..

[34]  E.A.B. da Silva,et al.  Comparative analysis of bitplane-based wavelet image coders , 2004 .

[35]  Stephan Olariu,et al.  Fast component labelling and convex hull computation on reconfigurable meshes , 1993, Image Vis. Comput..

[36]  Makoto Nagao,et al.  Structural analysis of natural textures by Fourier transformation , 1983, Comput. Vis. Graph. Image Process..

[37]  J. A. Catipovic,et al.  Compression techniques for improving underwater acoustic transmission of images and data , 1996, OCEANS 96 MTS/IEEE Conference Proceedings. The Coastal Ocean - Prospects for the 21st Century.

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

[39]  Mark S. Schmalz,et al.  Techniques for region coding in object-based image compression , 2004, SPIE Optics + Photonics.

[40]  Chong Sze Tong,et al.  Analysis of a hybrid fractal-predictive-coding compression scheme , 2003, Signal Process. Image Commun..

[41]  Yianni Attikiouzel,et al.  Two-Dimensional Linear Prediction Model-Based Decorrelation Method , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Gerhard X. Ritter,et al.  EBLAST: Efficient high-compression image transformation: II. Implementation and results , 2000, SPIE Optics + Photonics.

[43]  Majid Mirmehdi,et al.  Segmentation of Color Textures , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  Glenn Healey,et al.  Markov Random Field Models for Unsupervised Segmentation of Textured Color Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Mark S. Schmalz,et al.  Object-Based Image Compression , 2003, SPIE Optics + Photonics.

[46]  Hang Joon Kim,et al.  Support Vector Machines for Texture Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[47]  Alireza Khotanzad,et al.  Unsupervised Segmentation of Textured Images by Edge Detection in Multidimensional Feature , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  Zhuowen Tu,et al.  Image Segmentation by Data-Driven Markov Chain Monte Carlo , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  Xavier Cufí,et al.  Unsupervised active regions for multiresolution image segmentation , 2002, Object recognition supported by user interaction for service robots.

[50]  Nelson H. C. Yung,et al.  Highly accurate texture-based vehicle segmentation method , 2004 .

[51]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  Xiaoou Tang,et al.  Texture classification using wavelet packet and Fourier transforms , 1995, 'Challenges of Our Changing Global Environment'. Conference Proceedings. OCEANS '95 MTS/IEEE.

[53]  Joseph N. Wilson,et al.  Handbook of computer vision algorithms in image algebra , 1996 .

[54]  Joachim M. Buhmann,et al.  Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[55]  Lennart Thurfjell,et al.  A Boundary Approach for Fast Neighborhood Operations on Three-Dimensional Binary Data , 1995, CVGIP Graph. Model. Image Process..

[56]  A. Ravishankar Rao,et al.  Identifying High Level Features of Texture Perception , 1993, CVGIP Graph. Model. Image Process..

[57]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[58]  Asif Hayat,et al.  On reduction of input data for lossy compression of images , 2004 .

[59]  Farzin Deravi,et al.  Fractal Features and Their Application to Image Classification , 2003, CISST.

[60]  Gerhard X. Ritter,et al.  EBLAST: efficient high-compression image transformation: I. Background and theory , 1999, Optics + Photonics.

[61]  Paul F. Whelan,et al.  Experiments in colour texture analysis , 2001, Pattern Recognit. Lett..

[62]  Ashit Talukder,et al.  Texture analysis using partially ordered Markov models , 1994, Proceedings of 1st International Conference on Image Processing.

[63]  James S. Duncan,et al.  Deformable boundary finding influenced by region homogeneity , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.