Cover page and Table of Contents: PDF (size: 716KB)
Full Text (PDF, 716KB), PP.25-34
Views: 0 Downloads: 0
CBIR system, color histogram, edge histogram descriptor, Feature vector, genetic algorithm, Image retrieval, texture feature, query image
The evaluation of the performance of the Content Based Image Retrieval is undertaken for the consideration in this paper. Here the point of the discussion is the performance of the CBIR system using object oriented image segmentation and the evolutionary computational technique. The visual characteristics of the objects such as color, intensity and texture are extracted by the conventional methods. Object oriented image segmentation along with the evolutionary computational technique is proposed here for Image Retrieval Algorithm. Unsupervised Curve evolution method is used for object oriented segmentation of the Image and genetic Algorithm is used for the Optimum Classification and reduction in the Feature dimensionality. The Algorithm is tested on the images which are characterized by the low depth. The Berkeley database is found to be suitable for this purpose. The experimental result shows that the Genetic Algorithm enhances the performance of this Content Based Image Retrieval and found to be suitable for optimization of features selection and compression technique for Feature space.
Chandrashekhar G.Patil, Mahesh.T.Kolte, Prashant N.Chatur, Devendra S. Chaudhari,"Optimum Features selection by fusion using Genetic Algorithm in CBIR", IJIGSP, vol.7, no.1, pp.25-34, 2015. DOI: 10.5815/ijigsp.2015.01.04
N. Houhou, J. Thiran, and X. Bresson, "Fast texture segmentation model based on the shape operator and active contour," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1–8.
P. Puranik, P. Bajaj, A. Abraham, P. Palsodkar, and A. Deshmukh, "Human perception-based color image segmentation using comprehensive learning particle swarm optimization," in Proc. 2nd Int. Conf. Emerging Trends Eng. Technol., 2009, pp. 630–635.
S. Belongie, C. Carson, H. Greenspan, and J. Malik, "Color-and texturebased image segmentation using EM and its application to content-based image retrieval," in Proc. 6th Int. Conf. Comput. Vis., 1998, pp. 675–682.
H. Cheng, X. Jiang, Y. Sun, and J. Wang, "Color image segmentation: Advances and prospects," Pattern Recognit., vol. 34, no. 12, pp. 2259–2281, 2001.
J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888–905, Aug. 2000.
Jiangyuan Mei, Yulin Si, and Huijun Gao, "A Curve Evolution Approach for Unsupervised Segmentation of Images With Low Depth of Field", IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 10, OCTOBER 2013.
M. Swain and D. Ballard, "Color indexing," International Journal of Computer Vision, vol. 7, pp. 11-32, 1991.
J.-L. Shih and L.-H. Chen, "Color image retrieval based on primitives of color moments", Vision, Image and Signal Processing, IEE Proceedings, vol. 149, no. 6, 2002, pp. 370-376.
M. Rautiainen and D. Doermann, "Temporal Color Correlograms for Video Retrieval", Pattern Recognition, Proceedings of 16th International Conference, vol. 1, Aug. 2002, pp. 267-270.
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.
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.
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.
H. J. Zhang and D. Shong, "A scheme for visual feature-based image indexing," in Proceedings of SPIE Conference on Storage and Retrieval for Image and Video Databases III, vol. 2185 (San Jose, CA), pp. 36-46, February 1995.
Woo Chaw Seng, Seyed Hadi Mirisaee, "A Content-Based Retrieval System for Blood Cells Images," International Conference on Future Computer and Communication, 2009.
Rouhollah Rahmani, Sally A. Goldman, Hui Zhang, Sharath R. Cholleti, and Jason E. Fritts, "Localized Content-Based Image Retrieval", Ieee Transactions On Pattern Analysis And Machine Intelligence, Vol. 30, No. 11, November 2008.
A. Marakakis, N. Galatsanos, A. Likas and A. Stafylopatis," Probabilistic relevance feedback approach for content-based image retrieval based on gaussian mixture models," IET Image Process., 2009, Vol. 3, Iss. 1, pp. 10–25.
Wei Bian and Dacheng Tao, "Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval," IEEE Transactions On Image Processing, Vol. 19, No. 2, February 2010.
Ilea, D.E.; Whelan, P.F., "CTex—An Adaptive Unsupervised Segmentation Algorithm Based on Color-Texture Coherence," Image Processing, IEEE Transactions on, vol.17, no.10, pp.1926, 1939, Oct. 2008.
H. Li and K. Ngan, "Unsupervized video segmentation with low depth of field," IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 12, pp. 1742–1751, Dec. 2007.
Gabor, D., Theory of communication." In J. IEE, vol. 93, pp. 429-457, London, 1946.
Lin Yang, Peter Meer, David J. Foran, "Unsupervised Segmentation Based on Robust Estimation and Color Active Contour Models" IEEE Transactions On Image Processing, Vol. 19, No. 2, February 2010.