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A Generic Framework for Feature Representations in Image Categorization Tasks

A Generic Framework for Feature Representations in Image Categorization Tasks

Adam Csapo, Barna Resko, Morten Lind, Peter Baranyi
Copyright: © 2009 |Volume: 1 |Issue: 4 |Pages: 25
ISSN: 1942-9045|EISSN: 1942-9037|ISSN: 1942-9045|EISBN13: 9781616921170|EISSN: 1942-9037|DOI: 10.4018/jssci.2009062503
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MLA

Csapo, Adam, et al. "A Generic Framework for Feature Representations in Image Categorization Tasks." IJSSCI vol.1, no.4 2009: pp.36-60. http://doi.org/10.4018/jssci.2009062503

APA

Csapo, A., Resko, B., Lind, M., & Baranyi, P. (2009). A Generic Framework for Feature Representations in Image Categorization Tasks. International Journal of Software Science and Computational Intelligence (IJSSCI), 1(4), 36-60. http://doi.org/10.4018/jssci.2009062503

Chicago

Csapo, Adam, et al. "A Generic Framework for Feature Representations in Image Categorization Tasks," International Journal of Software Science and Computational Intelligence (IJSSCI) 1, no.4: 36-60. http://doi.org/10.4018/jssci.2009062503

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Abstract

The computerized modeling of cognitive visual information has been a research field of great interest in the past several decades. The research field is interesting not only from a biological perspective, but also from an engineering point of view when systems are developed that aim to achieve similar goals as biological cognitive systems. This article introduces a general framework for the extraction and systematic storage of low-level visual features. The applicability of the framework is investigated in both unstructured and highly structured environments. In a first experiment, a linear categorization algorithm originally developed for the classification of text documents is used to classify natural images taken from the Caltech 101 database. In a second experiment, the framework is used to provide an automatically guided vehicle with obstacle detection and auto-positioning functionalities in highly structured environments. Results demonstrate that the model is highly applicable in structured environments, and also shows promising results in certain cases when used in unstructured environments.

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