  Research is taking place to find effective algorithms for content-based image
representation and description. There is a substantial amount of algorithms
available that use visual features (color, shape, texture). Shape feature has
attracted much attention from researchers that there are many shape
representation and description algorithms in literature. These shape image
representation and description algorithms are usually not application
independent or robust, making them undesirable for generic shape description.
This paper presents an object shape representation using Kernel Density Feature
Points Estimator (KDFPE). In this method, the density of feature points within
defined rings around the centroid of the image is obtained. The KDFPE 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, which was compared with the DHFP to
prove its robustness.
