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.

[e-mail], [e-mail], [e-mail]

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

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