Image Representation Using Epanechnikov Density Feature Points Estimator
نویسندگان
چکیده
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 computer vision. In this paper we propose a generic image representation algorithm using Epanechnikov Density Feature Points Estimator (EDFPE). It is invariant to rotation, scale and translation. The image density feature points within defined rectangular rings around the gravitational centre of the image are obtained in the form of a vector. The EDFPE is applied to the vector representation of the image. The Cosine Angle Distance (CAD) algorithm is used to measure similarity of the images in the database. Quantitative evaluation of the performance of the system and comparison with other algorithms was done.
منابع مشابه
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 ...
متن کاملKernel 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...
متن کاملOn Convergence of Epanechnikov Mean Shift
Epanechnikov Mean Shift is a simple yet empirically very effective algorithm for clustering. It localizes the centroids of data clusters via estimating modes of the probability distribution that generates the data points, using the ‘optimal’ Epanechnikov kernel density estimator. However, since the procedure involves non-smooth kernel density functions, the convergence behavior of Epanechnikov ...
متن کاملHyperspectral Image Classification Based on the Fusion of the Features Generated by Sparse Representation Methods, Linear and Non-linear Transformations
The ability of recording the high resolution spectral signature of earth surface would be the most important feature of hyperspectral sensors. On the other hand, classification of hyperspectral imagery is known as one of the methods to extracting information from these remote sensing data sources. Despite the high potential of hyperspectral images in the information content point of view, there...
متن کاملEffectiveness of Image (dis)similarity Algorithms on Content- Based Image Retrieval
Dis) similarity measure is a significant component of vector model. In content based image retrieval the compatibility of (dis)similarity measure and representation technique is very important for effective and efficient image retrieval. In order to find a suitable dis-similarity measure for a particular representation technique experimental comparison is needed. This paper highlights some of t...
متن کامل