Commenced in January 2007
Paper Count: 31100
A Universal Model for Content-Based Image Retrieval
Abstract:In this paper a novel approach for generalized image retrieval based on semantic contents is presented. A combination of three feature extraction methods namely color, texture, and edge histogram descriptor. There is a provision to add new features in future for better retrieval efficiency. Any combination of these methods, which is more appropriate for the application, can be used for retrieval. This is provided through User Interface (UI) in the form of relevance feedback. The image properties analyzed in this work are by using computer vision and image processing algorithms. For color the histogram of images are computed, for texture cooccurrence matrix based entropy, energy, etc, are calculated and for edge density it is Edge Histogram Descriptor (EHD) that is found. For retrieval of images, a novel idea is developed based on greedy strategy to reduce the computational complexity. The entire system was developed using AForge.Imaging (an open source product), MATLAB .NET Builder, C#, and Oracle 10g. The system was tested with Coral Image database containing 1000 natural images and achieved better results.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072762Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2606
 Tristan Glatard, John Montagnat, "Texture based Medical image indexing and retrieval: application to cardiac images".
 B. S. Manjunath, Jens-Rainer Ohm, Vinod V. Vasudevan, and Akio Yamada, "Color and Texture Descriptors". In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, No. 6, June 2001, pp. 70-715.
 Zhe-Ming Lu1, Su-Zhi Li, and Hans Burkhardt, "A Content-Based Image Retrieval Scheme in JPEG Compressed Domain", International Journal of Innovative Computing, Information and Control ICIC International 2006 ISSN 1349-4198, Volume 2, Number 4, August 2006, pp. 831-839.
 Minyoung Eom, and Yoonsik Choe, "Fast Extraction of Edge Histogram in DCT Domain based on MPEG7", Proceedings of World Academy of Science, Engineering and Technology Volume 9 November 2005 ISSN 1307-6884, pp. 209-212.
 Son Lam Phung and Abdesselam Bouzerdoum, "A New Image Feature for Fast Detection of People in Images", International Journal of 2007 Institute for Scientific Information and Systems Sciences Computing and Information Volume 3, Number 3, pp. 383-391.
 Paul Stefan, et al.: Segmentation of Natural Images for CBIR.C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.
 P. S. Hiremath , Jagadeesh Pujari, "Content Based Image Retrieval using Color, Texture and Shape features", 15th International Conference on Advanced Computing and Communications, IEEE Computer Society 2007, pp. 780-784.
 Remco C. Veltcamp, Mirela Tanse, "Content Based Image Retrieval Systems". A Survey, Technical Report UU-CS-2000-34, October 2000, pp. 1-62.
 Mustafa Ozden and Ediz Polat, "Image Segmentation using Color and Texture features".
 John Montagnat, et al, "Texture-based Medical Image Indexing and Retrieval on Grids", Medical Imaging technology, vol 25 No. 5 Nov 2007. pp. 333-338.
 C. R. Shyu, et. al, "Local versus Global Features for Content-Based Image Retrieval", IEEE Workshop on Content-Based Access of Image and Video Libraries, 1998.
 Roger Weber and Michael Mlivoncic, "Efficient Region-Based Image Retrieval", ACM CIKM '03 November 3-8, 2003, USA.
 S. L. Phung and A. Bouzerdoum, "Detecting People in Images: An Edge Density Approach", IEEE, ICASSP 2007. pp. 1229-1232.
 Bohyung Han, Changjiang Yang, et al, "Bayesian Filtering and Integral Image for Visual Tracking".
 Vincent Arvis, Christophe Debain, et. al, "Generalization of the Cooccurrence Matrix for Color Images Application to Color Texture Classification", Image AnalStereol 2004, pp. 63-72.
 Alberto Amato, Vincenzo Di Lecce, "Edge Detection Techniques in Image Retrieval: The Semantic Meaning of Edge", 4th EURASIP Conference on Video/Image Processing and Multimedia Communications, Zagreb, Croatia. pp. 143-148.
 Thomas M. Lehmann, et al, "Automatic categorization of medical images for content-based retrieval and data mining", Computerized Medical Imaging and Graphics. Elsevier 2004. pp. 143-155.
 Dong Yin, Jia Pan, et al, "Medical Image Categorization based on Gaussian Mixture Model", IEEE 2008 International Conference on BioMedical Engineering and Informatics, pp. 128-131.
 Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng, "Fundamentals of Content-Based Image Retrieval" - http://research.microsoft.com/asia/dload_files/group/mcomputing/2003 P/ch01_Long_v40-proof.pdf