Probabilistic Learning of Visual Object Composition from Attended Segments

This paper proposes a model of probabilistic learning of object categories in conjunction with early visual processes of attention, segmentation and perceptual organization. This model consists of the following three sub-models: (1) a model of attention-mediated perceptual organization of segments, (2) a model of local feature representation of segments by using a bag of features, and (3) a model of learning object composition of categories based on intra-categorical probabilistic latent component analysis with variable number of classes and intercategorical typicality analysis. Through experiments by using images of plural categories in an image database, it is shown that the model learns a probabilistic structure of intra-categorical composition of objects and context and inter-categorical difference.

[1]  Serge J. Belongie,et al.  Object categorization using co-occurrence, location and appearance , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Masayasu Atsumi.  A Probabilistic Model of Visual Attention and Perceptual Organization for Constructive Object Recognition , 2009, ISVC.

[3]  Jun Zhang.  The mean field theory in EM procedures for Markov random fields , 1992, IEEE Trans. Signal Process..

[4]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[5]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[6]  Sto chastic Attentional Selection and Shift on the Visual Attention Pyramid , 2007 .

[7]  Bhiksha Raj,et al.  Probabilistic Latent Variable Models as Nonnegative Factorizations , 2008, Comput. Intell. Neurosci..

[8]  Pietro Perona,et al.  Selective visual attention enables learning and recognition of multiple objects in cluttered scenes , 2005, Comput. Vis. Image Underst..

[9]  Shlomo Geva.  K-tree: a height balanced tree structured vector quantizer , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

[10]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[11]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[12]  Andrea Vedaldi,et al.  Objects in Context , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[13]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[14]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.