An Enhanced Density Histogram of Feature Points representation method

An innovative way of object shape representation using Density Histogram of Feature Points (DHFP) is introduced and used in this paper. We have named this method Enhanced Density Histogram of Feature Points (EDHFP). We use silhouette images where the image region ξ consists of only those pixels that correspond to points on the object and have a value one (1) indicating “on” pixels. We count the number of “on” pixels in rectangle boundaries around a centroid, in the event that there are no “on” pixels in a rectangle boundary then the value is zero. The similarity level indicator is introduced to form part of vector a representation of the object shape. This method of image representation shows improved retrieval rate when compared to Density Histogram of Feature Points (DHFP) representation method. This method is capable of grouping object shapes with high probability of being similar using the similarity level indicator before calculation of similarity distance. Analytic analysis is done to justify our method, experiments are conducted and we compared our results with the object shape representation by DHFP to prove EDHFP´s robustness.