Image Description Techniques

Abstract

In the past few years, several applications in the areas of Multimedia information systems, CAD/CAM and computer graphics require to store and access large volumes of multimedia data such as images. Images can be associated with both low-level semantics (color, texture, shape, and various spatial constraints), and high-level semantics (correspondence between image objects and real-world objects). “In order to deal with these rich semantics of images, it is necessary to move from, image-level to object-level interpretation.”[91]. Therefore, a major data type stored and managed by these applications is the representation of two dimensional (2D) objects. Objects contain many features (e.g., color, texture, shape, etc.) that have meaningful semantics. From those features, shape is an important feature that conforms with the way human beings interpret and interact with the real world objects. Shape recognition has two major parts: shape description (representation) and shape matching. Shape description is an important issue in object recognition and its objective is to measure geometric attributes of an object, that can be used for classifying, matching, and recognizing objects. There are various methods for shape representation. There are also numerous shape matching approaches that have been proposed based upon the shape representation methods.