Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power.

[1]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[3]  Guojun Lu,et al.  A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval , 2003, J. Vis. Commun. Image Represent..

[4]  Paul M. de Zeeuw,et al.  Adaptive lifting for shape-based image retrieval , 2003, Pattern Recognit..

[5]  Zhang Hong-mei Segmentation of MRI Using Hierarchical Markov Random Field , 2002 .

[6]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Dorin Comaniciu Image segmentation using clustering with saddle point detection , 2002, Proceedings. International Conference on Image Processing.

[8]  Anil K. Jain Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[9]  Antonio Castelo,et al.  Topological approach for detecting objects from images , 2004, IS&T/SPIE Electronic Imaging.

[10]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  S. Mallat A wavelet tour of signal processing , 1998 .

[12]  Erkki Oja,et al.  Statistical Shape Features for Content-Based Image Retrieval , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[13]  Roberto Marcondes Cesar Junior,et al.  Morphometrical data analysis using wavelets , 2004, Real Time Imaging.

[14]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[15]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Leonidas Palios,et al.  An efficient shape-based approach to image retrieval , 2000, Pattern Recognit. Lett..

[17]  E. Brigham,et al.  The fast Fourier transform and its applications , 1988 .

[18]  Stanislaw Osowski,et al.  Fourier and wavelet descriptors for shape recognition using neural networks - a comparative study , 2002, Pattern Recognit..

[19]  O. Bruno,et al.  Leaf shape analysis using the multiscale Minkowski fractal dimension, a new morphometric method: a study with Passiflora (Passifloraceae) , 2005 .

[20]  Batista,et al.  An adaptive gradient-based boundary detector for MRI images of the brain , 1999 .

[21]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[22]  Gérard G. Medioni,et al.  Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  O. Bruno,et al.  Comparison of shape analysis methods for Guinardia citricarpa ascospore characterization , 2005 .

[24]  Rosane Minghim,et al.  Morse operators for digital planar surfaces and their application to image segmentation , 2004, IEEE Transactions on Image Processing.

[25]  Mark O'Malley,et al.  Segmentation of plant cell pictures , 1993, Image Vis. Comput..

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

[27]  Luciano da Fontoura Costa,et al.  Shape Analysis and Classification: Theory and Practice , 2000 .

[28]  Yi-Ping Hung,et al.  A New Image Segmentation Method for Removing Background of Object Movies by Learning Shape Priors , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[29]  Erkki Oja,et al.  Analyzing Low-Level Visual Features Using Content-Based Image Retrieval , 2000 .

[30]  Mohamed S. Kamel,et al.  Shape-based image retrieval applied to trademark images , 2004 .

[31]  Shinji Ozawa,et al.  Efficient Wavelet-Based Image Retrieval Using Coarse Segmentation and Fine Region Feature Extraction , 2005, IEICE Trans. Inf. Syst..