An implementation of a CBIR system based on SVM learning scheme

References

• Kato, T., 1992, Database architecture for content-based image retrieval. Proceedings of SPIE Image Storage and Retrieval Systems, 1662, 112–123, San Jose, CA, USA.
   Google Scholar
• Muller, H., Michoux, N., Bandon, D., Geissbuhler, A., 2004, A review of content-based image retrieval systems in medical applications - Clinical benefits and future directions. International Journal of Medical Informatics, 73, 1–23.
   PubMed Web of Science ®Google Scholar
• Isgum, I., van Ginneken, B., Olree, M., 2004, Automatic detection of calcications in the aorta from CT scans of the abdomen. Academic Radiology, 11, 247–257.
   PubMedGoogle Scholar
• Sbober, A., Eccher, C., Blanzieri, E., Bauer, P., Cristifolini, M., Zumiani, G., Forti, S., 2003, A multiple classifier system for early melanoma diagnosis. Artificial Intelligence in Medicine, 27, 29–44.
   PubMedGoogle Scholar
• Vailaya, A., Figueiredo, M.A.T., Jain, A.K., Zhang, H.J., 2001, Image classification for content-based indexing. IEEE Transactions Image Processing, 10, 117–130.
   PubMedGoogle Scholar
• Han, J., Kuang, K., 2000, Fuzzy colour histogram and its use in colour image retrieval. IEEE Transactions Image Process, 11, 944–952.
   Google Scholar
• Shi, J., Malik, J., 2000, Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 888–905.
   Web of Science ®Google Scholar
• Fernandez, J., Aranda, J., 2000, Image segmentation combining region depth and object features. 15th International Conference on Pattern Recognition, Vol. 1, pp. 618–621
   Google Scholar
• Sheikholeslami, G.H., Chang, W., Zhang, A., 2002, Semantic clustering and querying on heterogeneous features for visual data. IEEE Transactions Knowledge and Data Engineering, 14, 988–1002.
   Web of Science ®Google Scholar
• Sang, S., Liu, M., Liu, J., An, Q., 2010, A new approach for texture classification in CBIR. International Journal of Computer Applications in Technology, 38, 34–39.
   Google Scholar
• Reddy, P.V.N., Prasad, K.S., 2010, Multiwavelet based texture features for content-based image retrieval. International Journal of Power Control Signal and Computation, 1.
   Google Scholar
• Hafner, J., Sawhney, H.S., Equits, W., Flickner, M., Niblack, W., 1995, Efficient colour histogram indexing for quadratic form distance functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17.
   Google Scholar
• Smith, J.R., Chang, S.F., 1995, Automated image retrieval using colour and texture. IEEE Transaction on Pattern Analysis and Machine Intelligence.
   Google Scholar
• Dahshan, A.E., Salem, M., Younis, T.H., 2009, A hybrid technique for automatic MRI brain images classification. Journal of Informatica.
   Google Scholar
• Epischina, J., de Albuquerque, A., Deserno, T.M., 2011, Content-based image retrieval applied to BI-RADS tissue classification in screening mammography. World Journal of Radiology, 28, 24–31.
   Google Scholar
• Rafi Nazari, M., Fatemizadeh, E., 2010, A CBIR system for human brain magnetic resonance image indexing. International Journal of Computer Applications, 7, 975–8887.
   Google Scholar
• Jayech, K.H., Mahjoub, M.A., 2010, New approach using bayesian network to improve content based image classification systems. International Journal of Computer Science Issues, 7.
   Google Scholar
• Chaplot, S., Patnaik, L.M., Jagannathan, N.R., 2006, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomedical Signal Processing and Control, 1, 86–92.
   Google Scholar
• Maitra, M., Chatterjee, A., 2007, Hybrid multi resolution Slantlet transform and fuzzy c- means clustering approach for normal-pathological brain MR image segregation. Medical Engineering and Physics, 30, 615–623.
   PubMedGoogle Scholar
• Kannan, A., Mohan, V., Anbazhagan, N., 2010, Image clustering and retrieval using image mining techniques. IEEE International Conference on Computational Intelligence and Computing Research.
   Google Scholar
• Zakariya, S.M., Ali, R., Ahmad, N., 2010, Unsupervised content based image retrieval by combining visual features of an image with a threshold. International Conference on Computer Technology, 2, vol. 2 Special Issue of IJCCT.
   Google Scholar
• Harvard Medical School website. www.cma.mgh.harvard.edu/ibsr/.
   Google Scholar
• Haralick, R.M., 1979, Statistical and structural approach to texture. Proceeding of the IEEE, 67, 786–804.
   Web of Science ®Google Scholar
• Zhang, H., Zhong, D., 1995, A scheme for visual feature based image indexing. Proceedings of the SPIE Conference on Storage and Retrieval for Image and Video Databases, pp. 278–288.
   Google Scholar
• Vailaya, A., et al., 1998, On image classification: city vs landscapes. Journal of Pattern Recognition, 31, 1921–1935.
   Web of Science ®Google Scholar
• Vailaya, A., et al., 2001, Bayesian framework for hierarchical semantic classification of vacation images. IEEE Transactions on Image Processing, 10, 117–130.
   PubMedGoogle Scholar
• Szummer, M., Picard, R., 1998, Indoor-outdoor image classification. Proceedings of the Workshop in Content-based Access to Image and Video Databases, Bombay, India.
   Google Scholar