Machine learning of visual object categorization: an application of the SUSTAIN model.

Giovanni Sirio CarmantiniPlymouth University, Plymouth, United Kingdom
Angelo CangelosiPlymouth University, Plymouth, United Kingdom
Andy WillsPlymouth University, Plymouth, United Kingdom

Abstract

Formal models of categorization are psychological theories that try to describe the process of categorization in a lawful way, using the language of mathematics. Their mathematical formulation makes it possible for the models to generate precise, quantitative predictions. SUSTAIN (Love, Medin & Gureckis, 2004) is a powerful formal model of categorization that has been used to model a range of human experimental data, describing the process of categorization in terms of an adaptive clustering principle. Love et al. (2004) suggested a possible application of the model in the field of object recognition and categorization. The present study explores this possibility, investigating at the same time the utility of using a formal model of categorization in a typical machine learning task. The image categorization performance of SUSTAIN on a well-known image set is compared with that of a linear Support Vector Machine, confirming the capability of SUSTAIN to perform image categorization with a reasonable accuracy, even if at a rather high computational cost.

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Machine learning of visual object categorization: an application of the SUSTAIN model. (2.4 MB)



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