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Genetic Algorithms and Other Approaches in Image Feature Extraction and Representation
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Author(s): Danilo Avola (Institute of Research on Population and Social Policies - National Research Council, Italy), Fernando Ferri (Institute of Research on Population and Social Policies - National Research Council, Italy)and Patrizia Grifoni (Institute of Research on Population and Social Policies - National Research Council, Italy)
Copyright: 2009
Pages: 19
Source title:
Artificial Intelligence for Maximizing Content Based Image Retrieval
Source Author(s)/Editor(s): Zongmin Ma (Northeastern University, China)
DOI: 10.4018/978-1-60566-174-2.ch001
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Abstract
The novel technologies used in different application domains allow obtaining digital images with a high complex informative content, which can be exploited to interpret the semantic meaning of the images themselves. Furthermore, it has to be taken into account that the complex informative content extracted from the images, that is, the features, need of flexible, powerful, and suitable ways to be represented and managed. The metadata through which a set of images can be described are directly tied to the quality and quantity of the extracted features; besides the efficient management of the metadata depend on the practical and capable feature representation. The more used approaches to analyze the image content do not seem able to provide an effective support to obtain a whole image understanding and feature extraction process. For this reason, new classes of methodologies that involve computational intelligent approaches have been developed. In particular, genetic algorithms (GAs) and other artificial intelligent- (AI) based approaches seem to provide the best suitable solutions. The artificial intelligent technologies allow for the obtaining of a more semantically complex metadata image representation through which to develop advanced systems to retrieval and to handle the digital images. This new method to conceive a metadata description allows the user to make queries in a more natural, detailed, and semantically complete way. As a result it can overcome the always more sophisticated duties caused by the use of wide local and/or distributed databases with heterogeneous complex images.
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