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Unsupervised Categorization of Objects into Artificial and Natural Superordinate Classes Using Features from Low-Level Vision
Zahra Sadeghi, Majid Nili Ahmadabadi, Babak Nadjar Araabi
Pages - 339 - 352     |    Revised - 15-08-2013     |    Published - 15-09-2013
Volume - 7   Issue - 4    |    Publication Date - September 2013  Table of Contents
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KEYWORDS
Objects' Super-class Categorization, Low Level Visual Features, Categorization of Objects to Artificial and Natura, Local and Global Features, Color, Orientation, Frequency.
ABSTRACT
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.
CITED BY (2)  
1 Sadeghi, Z., Nadjar Araabi, B., & Nili Ahmadabadi, M. (2015). A Computational Approach towards Visual Object Recognition at Taxonomic Levels of Concepts. Computational intelligence and neuroscience, 2015.
2 Sadeghi, Z. (2015). Children’s Line Drawings and Object Representation Strategies: Categorization of Children’s Mental Representation Strategies According to the Existing Theories for Object Recognition by Studying Line Drawings.
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Miss Zahra Sadeghi
Cognitive Robotics Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box: 14395-515, Iran Institute for Research in Fundamental Sciences, Tehran, P.O. Box 19395-5746, Iran - Iran
zahra.sadeghi@ut.ac.ir
Mr. Majid Nili Ahmadabadi
Cognitive Robotics Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box: 14395-515, Iran Institute for Research in Fundamental Sciences, Tehran, P.O. Box 19395-5746, Iran - Iran
Mr. Babak Nadjar Araabi
Cognitive Robotics Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box: 14395-515, Iran Institute for Research in Fundamental Sciences, Tehran, P.O. Box 19395-5746, Iran - Iran


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