Image Representation Using EPANECHNIKOV Density Feature Points Estimator

Image Representation Using Epanechnikov Density Feature Points Estimator

In image retrieval most of the existing visual content based representation methods are usually application dependent or non robust, making them not suitable for generic applications. These representation methods use visual contents such as colour, texture, shape, size etc. Human image recognition is largely based on shape, thus making it very appealing for image representation algorithms in co...

Object Shape Representation by Kernel Density Feature Points Estimator

This paper introduces an object shape representation using Kernel Density Feature Points Estimator (KDFPE). In this method we obtain the density of feature points within defined rings around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of image representation ...

Kernel Density Feature Points Estimator for Content-Based Image Retrieval

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 des...

Novel Image Representation and Description Technique using Density Histogram of Feature Points

This paper introduces novel object shape representation using Density Histogram of Feature Points (DHFP). 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 a rectangle boundary around the centroid, in the event that there are no “on” pixels i...

Content-Based Image Retrieval: Object representation by the Density of Feature Points