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Paper   IPM / Cognitive Sciences / 13441
School of Cognitive Sciences
  Title:   Unsupervised Categorization of Objects into Artificial and Natural Superordinate Classes Using Features from Low-Level Vision
1.  Z. Sadeghi
2.  M. Nili Ahmadabadi
3.  B. Nadjar Araabi
  Status:   Published
  Journal: International Journal of Image processing (IJIP)
  Vol.:  7
  Year:  2013
  Pages:   339-352
  Supported by:  IPM
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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