Kernel Density Feature Points Estimator for Content-Based Image Retrieval
This work addresses the need for robust, application-independent shape description in image retrieval, though it appears incremental as it builds on existing feature point density approaches.
The paper tackles the problem of generic shape representation for content-based image retrieval by proposing a Kernel Density Feature Points Estimator (KDFPE) method, which achieves an improved retrieval rate compared to the Density Histogram Feature Points method.
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.