IMAGE FEATURE EXTRACTION An image feature is a distinguishing primitive characteristic or attribute of an image. Some features are natural in the sense that such features are defined by the visual appearance of an image, while other, artificial features result from specific manipulations of an image. Natural features include the luminance of a region of pixels and gray scale textural regions. Image amplitude histograms and spatial frequency spectra are examples of artificial features. | Digital Image Processing PIKS Inside Third Edition. William K. Pratt Copyright 2001 John Wiley Sons Inc. ISBNs 0-471-37407-5 Hardback 0-471-22132-5 Electronic 16 IMAGE FEATURE EXTRACTION An image feature is a distinguishing primitive characteristic or attribute of an image. Some features are natural in the sense that such features are defined by the visual appearance of an image while other artificial features result from specific manipulations of an image. Natural features include the luminance of a region of pixels and gray scale textural regions. Image amplitude histograms and spatial frequency spectra are examples of artificial features. Image features are of major importance in the isolation of regions of common property within an image image segmentation and subsequent identification or labeling of such regions image classification . Image segmentation is discussed in Chapter 16. References 1 to 4 provide information on image classification techniques. This chapter describes several types of image features that have been proposed for image segmentation and classification. Before introducing them however methods of evaluating their performance are discussed. . IMAGE FEATURE EVALUATION There are two quantitative approaches to the evaluation of image features prototype performance and figure of merit. In the prototype performance approach for image classification a prototype image with regions segments that have been independently categorized is classified by a classification procedure using various image features to be evaluated. The classification error is then measured for each feature set. The best set of features is of course that which results in the least classification error. The prototype performance approach for image segmentation is similar in nature. A prototype image with independently identified regions is segmented by a 509 510 IMAGE FEATURE EXTRACTION segmentation procedure using a test set of features. Then the detected segments are compared
