Cbir
Shape based image retrieval through fuzzy c means classifier
*Ajay B. Kurhe **Suhas S. Satonkar ***Prakash B. Khanale
*Department of computer science, S.G.B. College, Purna, Dist. Parbhani (M.S.) India.
**S.J.College, Gangakhed, Dist. Parbhani (M.S.) India.
**D.S.M. College, Parbhani (M.S.) India.
ajay.kurhe@rediffmail.com, Suhas.satonkar@gmail.com, pb_khanale@gmail.com
Abstract: Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using fuzzy C means classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of Coil database images; at the highest level, images are classified as belonged to query image class. We demonstrate that the features used in the classifier are obtained from the directional chain code information of the boundaries of the objects. The bounding box of an object is segmented into four blocks for the spatial relations and the chain code histogram is computed in each of the blocks. Based on the chain code histogram, here we have used 16 dimensional features for recognition. These chain code features are fed to the fuzzy C means classifier for recognition. Best recognition result we obtained is 98%.
1. INTRODUCTION
We address the problem of two-dimensional (2-D) shape representation and matching for large image databases. The boundary contour of the object must include the boundary part which is entirely inside the outline of the object. For this experiment we used canny function for boundary extraction. In this work chain code is main feature as shown in fig.1 which is basic element of boundary, from which boundaries main parts such as curves, angles, vertical lines, horizontal lines, cross lines are formed. For spatial relations we divide whole boundary
*Ajay B. Kurhe **Suhas S. Satonkar ***Prakash B. Khanale
*Department of computer science, S.G.B. College, Purna, Dist. Parbhani (M.S.) India.
**S.J.College, Gangakhed, Dist. Parbhani (M.S.) India.
**D.S.M. College, Parbhani (M.S.) India.
ajay.kurhe@rediffmail.com, Suhas.satonkar@gmail.com, pb_khanale@gmail.com
Abstract: Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using fuzzy C means classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of Coil database images; at the highest level, images are classified as belonged to query image class. We demonstrate that the features used in the classifier are obtained from the directional chain code information of the boundaries of the objects. The bounding box of an object is segmented into four blocks for the spatial relations and the chain code histogram is computed in each of the blocks. Based on the chain code histogram, here we have used 16 dimensional features for recognition. These chain code features are fed to the fuzzy C means classifier for recognition. Best recognition result we obtained is 98%.
1. INTRODUCTION
We address the problem of two-dimensional (2-D) shape representation and matching for large image databases. The boundary contour of the object must include the boundary part which is entirely inside the outline of the object. For this experiment we used canny function for boundary extraction. In this work chain code is main feature as shown in fig.1 which is basic element of boundary, from which boundaries main parts such as curves, angles, vertical lines, horizontal lines, cross lines are formed. For spatial relations we divide whole boundary
References: [1] I. Ahmad and W. I. Grosky, (Sept. 2003) “Indexing and retrieval of images by spatial constraint”, Journal of Visual Communication and Image Representation, 14(3):291–320. [2] Imran Ahmad, Muhammad Talal Ibrahim, 2006, “Image Classification and Retrieval using Correlation”, Proceedings of the 3rd Canadian Conference on Computer and Robot Vision [6] Sami Brandt, Jorma Laaksonen and Erkki Oja, (2000), “Statistical Shape Features in Content- Based Image Retrieval”, 0-7695-0750-6/0$01 0.00 0 IEEE. [7] P. B. Khanale and A. B. Kurhe, (2011), “Color Perception of Images using Fuzzy Logic”, Advances in Computational Sciences and Technology, Volume 4 Number 1pp [8] Ahmed, Mohamed N.; Yamany, Sameh M.; Mohamed, Nevin; Farag, Aly A.; Moriarty, Thomas (2002) [9] Nock, R. and Nielsen, F. (2006) "On Weighting Clustering", IEEE Trans. on Pattern Analysis and Machine Intelligence, 28 (8), 1–13 [10] Bezdek, James C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. ISBN 0-306- 40671-3 [11] A. Pentland, R.W. Picard, and S. Sclaroff, (1994), “Photobook: Tools for Content-Based Manipulation of Image Databases”, In Storage and Retrieval for Image and Video Databases II, [12] A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, (Dec. 2000), “Content-based image retrieval at the end of the early years”, IEEE Transaction on Pattern Analysis and Machine [13] J. R. Smith and S.-F. Chang, (1996), “VisualSEEk: A Fully Automated Content-Based Image Query System”, In Proceedings of ACM Conference on Multimedia, pages 211–218. [14] URL. http://www.lems.brown.edu/˜dmc, 2006. [15] B. M. Wei-Ying Ma., (1999), “NeTra: A Toolbox for Navigating Large Image Database”, Multimedia Systems, 7(3):184–198.