Integrated color, texture and shape information for content-based image retrieval

Feature extraction and the use of the features as query terms are crucial problems in content-based image retrieval (CBIR) systems. The main focus in this paper is on integrated color, texture and shape extraction methods for CBIR. We have developed original CBIR methodology that uses Gabor filtration for determining the number of regions of interest (ROIs), in which fast and effective feature extraction is performed. In the ROIs extracted, texture features based on thresholded Gabor features, color features based on histograms, color moments in YUV space, and shape features based on Zernike moments are then calculated. The features presented proved to be efficient in determining similarity between images. Our system was tested on postage stamp images and Corel photo libraries and can be used in CBIR applications such as postal services.

[1]  D. Sagi,et al.  Gabor filters as texture discriminator , 1989, Biological Cybernetics.

[2]  Nikolay Petkov,et al.  Biologically motivated computationally intensive approaches to image pattern recognition , 1995, Future Gener. Comput. Syst..

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

[4]  Alberto Sanfeliu,et al.  Progress in Pattern Recognition, Speech and Image Analysis , 2003, Lecture Notes in Computer Science.

[5]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Proceedings of International Conference on Image Processing.

[6]  Stefan M. Rüger,et al.  Evaluation of Texture Features for Content-Based Image Retrieval , 2004, CIVR.

[7]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Nicu Sebe,et al.  Image retrieval using wavelet-based salient points , 2001, J. Electronic Imaging.

[9]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.

[11]  Nicolai Petkov,et al.  Nonlinear operator for oriented texture , 1999, IEEE Trans. Image Process..

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

[13]  Amarnath Gupta,et al.  Virage image search engine: an open framework for image management , 1996, Electronic Imaging.

[14]  Ryszard S. Choras,et al.  Content-Based Retrieval Using Color, Texture, and Shape Information , 2003, CIARP.

[15]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[16]  Hongbin Zha,et al.  Combining interest points and edges for content-based image retrieval , 2005, IEEE International Conference on Image Processing 2005.

[17]  Dennis Gabor,et al.  Theory of communication , 1946 .

[18]  A. Murat Tekalp,et al.  Integration of color, edge, shape, and texture features for automatic region-based image annotation and retrieval , 1998, J. Electronic Imaging.

[19]  Nicu Sebe,et al.  Content-based image retrieval using wavelet-based salient points , 2000, IS&T/SPIE Electronic Imaging.

[20]  Tomasz Andrysiak,et al.  Image retrieval based on hierarchical Gabor filters , 2005 .

[21]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Jean-Michel Jolion,et al.  Content based image retrieval using interest points and texture features , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

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

[24]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[25]  Nicolai Petkov,et al.  Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells , 1997, Biological Cybernetics.

[26]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[27]  Babu M. Mehtre,et al.  CORE: a content-based retrieval engine for multimedia information systems , 1995, Multimedia Systems.

[28]  Ryszard S. Choras,et al.  Content Based Image Retrieval Technique , 2005, CORES.

[29]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[30]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[31]  Peter Alshuth,et al.  IRIS - Image retrieval for images and videos , 1996 .

[32]  Jonathon S. Hare,et al.  Salient Regions for Query by Image Content , 2004, CIVR.