Tranos Zuva¹, Oludayo O. Olugbara², Sunday O. Ojo³ and Seleman M.Ngwira¹, ¹Tshwane University of Technology, South Africa, ²Durban University of Technology, South Africa and ³Faculty of Information and Communication Technology, South Africa

This paper introduces an object shape representation using Kernel Density Feature Points Estimator (KDFPE). In this method we obtain the density of feature points within defined rings around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of image representation shows improved retrieval rate when compared to Density Histogram Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.

Kernel Density Function, Similarity, Image Representation, Segmentation, Density Histogram