“School of Cognitive%20Sciences”
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| Paper IPM / Cognitive%20Sciences / 13441 |
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| Abstract: | |||||||
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Object recognition problem has mainly focused on classification of specific object classes and not
much work is devoted to the problem of automatic recognition of general object classes. The aim
of this paper is to distinguish between the highest levels of conceptual object classes (i.e. artificial
vs. natural objects) by defining features extracted from energy of low level visual characteristics
of color, orientation and frequency. We have examined two modes of global and local feature
extraction. In local strategy, only features from a limited number of random small windows are
extracted, while in global strategy, features are taken from the whole image.
Unlike many other object recognition approaches, we used unsupervised learning technique for
distinguishing between two classes of artificial and natural objects based on experimental results
which show that distinction of visual object super-classes is not based on long term memory.
Therein, a clustering task is performed to divide the feature space into two parts without
supervision. Comparison of clustering results using different sets of defined low level visual
features show that frequency features obtained by applying Fourier transfer could provide the
highest distinction between artificial and natural objects.
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