Classifications of image features: a survey

Abstract

Computer imaging is a complex multi-discipline science with broad application and well developed theory. A brief knowledge of computer imaging is presented in this paper. An image feature is a descriptor of an image, which can avoid redundant data and reduce the effects of noise and variance. In computer imaging, feature selection is vital for researchers and processors. Feature extraction and image processing are based on the mathematical selection, computation and manipulation of image features with high efficiency, robustness and invariance. Common image features are expressed under definitions of feature measurements, which is stated in this paper. This paper mainly brings an overall presentation of different sorts of image features and classifies them into specified types. Based on different purposes of application, three main ways are put forward in this paper to categorize image features. The first one is based on the nature of the image. The features applied to a binary image are different from the ones applied to a gray-level image or a color image. The second classification separates visible features from invisible features. The last one classifies image features into global image features and local image features. A clear statement is given for each way of classification and each type of image feature. Every image feature has both merits and defects, hence when selecting features for further image application, a clear cognition of different features is required. Well applied image features and the algorithms related to them are highlighted in this paper with analysis and comparison.