Component Analysis in Artificial Vision

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Author(s): Oscar Déniz Suárez (University of Las Palmas de Gran Canaria, Spain)and Gloria Bueno García (University of Castilla – La Mancha, Spain)
Copyright: 2009
Pages: 5
Source title: Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch056

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

The typical recognition/classification framework in Artificial Vision uses a set of object features for discrimination. Features can be either numerical measures or nominal values. Once obtained, these feature values are used to classify the object. The output of the classification is a label for the object (Mitchell, 1997). The classifier is usually built from a set of “training” samples. This is a set of examples that comprise feature values and their corresponding labels. Once trained, the classifier can produce labels for new samples that are not in the training set. Obviously, the extracted features must be discriminative. Finding a good set of features, however, may not be an easy task. Consider for example, the face recognition problem: recognize a person using the image of his/her face. This is currently a hot topic of research within the Artificial Vision community, see the surveys (Chellappa et al, 1995), (Samal & Iyengar, 1992) and (Chellappa & Zhao, 2005). In this problem, the available features are all of the pixels in the image. However, only a number of these pixels are normally useful for discrimination. Some pixels are background, hair, shoulders, etc. Even inside the head zone of the image some pixels are less useful than others. The eye zone, for example, is known to be more informative than the forehead or cheeks (Wallraven et al, 2005). This means that some features (pixels) may actually increase recognition error, for they may confuse the classifier. Apart from performance, from a computational cost point of view it is desirable to use a minimum number of features. If fed with a large number of features, the classifier will take too long to train or classify.

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