Image Annotation by Moments

The rapid growth of the Internet and multimedia information has generated a need for technical indexing and searching of multimedia information, especially in image retrieval. Image searching systems have been developed to allow searching in image databases. However, these systems are still inefficient in terms of semantic image searching by textual query. To perform semantic searching, it is necessary to be able to transform the visual content of the images (colours, textures, shapes) into semantic information. This transformation, called image annotation, assigns a legend or keywords to a digital image. The traditional methods of image retrieval rely heavily on manual image annotation which is very subjective, very expensive and impossible given the size and the phenomenal growth of currently existing image databases. Therefore it is quite natural that the research has emerged in order to find a computing solution to the problem. It is thus that research work has quickly bloomed on the automatic image annotation, aimed at reducing both the cost of annotation and the semantic gap between semantic concepts and digital low-level features. One of the approaches to deal with image annotation is image classification. From the segmented image, the feature vector is calculated and fed to the classifier in order to choose the appropriate keyword for each region that represents the image content. In this chapter, the use of Hu, Zernike and Legendre moments as feature extraction will be presented.