Feature Extraction and Representation
What will we learn?
- What is feature extraction and why is it a critical step in most computer vision and image processing solutions?
- Which types of features can be extracted from an image and how is this usually done?
- How are the extracted features usually represented for further processing?
This chapter discusses methods and techniques for representing and describing an image and its objects or regions of interest. Most techniques presented in this chapter assume that an image has undergone segmentation.1 The common goal of feature extraction and representation techniques is to convert the segmented objects into representations that better describe their main features and attributes. The type and complexity of the resulting representation depend on many factors, such as the type of image (e.g., binary, grayscale, or color), the level of granularity (entire image or individual regions) desired, and the context of the application that uses the results (e.g., a two-class pattern classifier that tells circular objects from noncircular ones or an image retrieval system that retrieves images judged to be similar to an example image).
Feature extraction is the process by which certain features of interest within an image are detected and represented for further processing. It is a critical step in most computer vision and image processing solutions because it marks the transition from pictorial to nonpictorial (alphanumerical, ...