Please use this identifier to cite or link to this item: http://hdl.handle.net/10321/980
Title: Introducing an adaptive kernel density feature points estimator for image representation
Authors: Zuva, Tranos
Olugbara, Oludayo O. 
Ojo, Sunday O.
Ngwira, Seleman M.
Keywords: Kernel Density Function;Similarity;Image Representation;Segmentation;Density Histogram
Issue Date: Jun-2012
Publisher: IJITCS
Source: Tranos Z.; Oludayo, O.O.; Sunday, O.O. and Seleman, M.N. 2012. Introducing an Adaptive Kernel Density Feature Points Estimator for Image Representation. International Conference on Computer Science, Engineering and Technology.
Abstract: This paper provides an image shape representation technique known as Adaptive Kernel Density Feature Points Estimator (AKDFPE). In this method, the density of feature points within defined rings (bandwidth) around the centroid of the image is obtained in the form of a vector. The AKDFPE is then applied to the vector of the image. AKDFPE is invariant to translation, scale and rotation. This method of image representation shows improved retrieval rate when compared to Kernel Density Feature Points Estimator (KDFPE) method. Analytic analysis is done to justify our method, which was compared with the KDFPE to prove its robustness.
URI: http://hdl.handle.net/10321/980
ISSN: 2091-0266
Appears in Collections:Research Publications (Accounting and Informatics)

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