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A Generic Framework for Feature Representations in Image Categorization Tasks
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Author(s): Adam Csapo (Budapest University of Technology and Economics, Hungary), Barna Resko (Hungarian Academy of Sciences, Hungary), Morten Lind (NTNU, Dept. of Production and Quality Engineering, Norway), Peter Baranyi (Budapest University of Technology and Economics, Hungary, & Hungarian Academy of Sciences, Hungary)and Domonkos Tikk (Budapest University of Technology and Economics, Hungary)
Copyright: 2012
Pages: 22
Source title:
Software and Intelligent Sciences: New Transdisciplinary Findings
Source Author(s)/Editor(s): Yingxu Wang (University of Calgary, Canada)
DOI: 10.4018/978-1-4666-0261-8.ch029
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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 paper 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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