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Research Publications (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/212

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Author: search.filters.author.Ngwira, Seleman M.

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    Kernel density feature points estimator for content-based image retrieval
    (AIRCC, 2012-02) Zuva, Tranos; Olugbara, Oludayo O.; Ojo, Sunday O.; Ngwira, Seleman M.
    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.
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    Introducing an adaptive kernel density feature points estimator for image representation
    (IJITCS, 2012-06) Zuva, Tranos; Olugbara, Oludayo O.; Ojo, Sunday O.; Ngwira, Seleman M.
    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.