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G. Griffin, A. Holub and P. Perona, “Caltech-256 Object Category Dataset,” Technical Report 7694, California Institute of Technology, Pasadena, 2007.

has been cited by the following article:

• TITLE: Attention-Guided Organized Perception and Learning of Object Categories Based on Probabilistic Latent Variable Models

  AUTHORS: Masayasu Atsumi

  KEYWORDS: Attention; Perceptual Organization; Probabilistic Learning; Object Categorization

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.2, May 22, 2013

  ABSTRACT: This paper proposes a probabilistic model of object category learning in conjunction with attention-guided organized perception. This model consists of a model of attention-guided organized perception of object segments on Markov random fields and a model of learning object categories based on a probabilistic latent component analysis. In attention guided organized perception, concurrent figure-ground segmentation is performed on dynamically-formed Markov random fields around salient preattentive points and co-occurring segments are grouped in the neighborhood of selective attended segments. In object category learning, a set of classes of each object category is obtained based on the probabilistic latent component analysis with the variable number of classes from bags of features of segments extracted from images which contain the categorical objects in context and an object category is represented by a composite of object classes. Through experiments using two image data sets, it is shown that the model learns a probabilistic structure of intra-categorical composition and inter-categorical difference of object categories and achieves high performance in object category recognition.