Unsupervised learning of visual taxonomies

As more images and categories become available, organizing them becomes crucial. We present a novel statistical method for organizing a collection of images into a tree-shaped hierarchy. The method employs a non-parametric Bayesian model and is completely unsupervised. Each image is associated with a path through a tree. Similar images share initial segments of their paths and therefore have a smaller distance from each other. Each internal node in the hierarchy represents information that is common to images whose paths pass through that node, thus providing a compact image representation. Our experiments show that a disorganized collection of images will be organized into an intuitive taxonomy. Furthermore, we find that the taxonomy allows good image categorization and, in this respect, is superior to the popular LDA model.

[1]  S. MacEachern,et al.  Bayesian Density Estimation and Inference Using Mixtures , 2007 .

[2]  Alexei A. Efros,et al.  Discovering object categories in image collections , 2005 .

[3]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  Gang Wang,et al.  Using Dependent Regions for Object Categorization in a Generative Framework , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Radford M. Neal,et al.  A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model , 2004 .

[6]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[7]  Donald Geman,et al.  A Design Principle for Coarse-to-Fine Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[8]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

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

[10]  Narendra Ahuja,et al.  Learning the Taxonomy and Models of Categories Present in Arbitrary Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[11]  M. Escobar,et al.  Bayesian Density Estimation and Inference Using Mixtures , 1995 .

[12]  Trevor Darrell,et al.  The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[13]  Antonio Torralba,et al.  Shared Features for Multiclass Object Detection , 2006, Toward Category-Level Object Recognition.

[14]  Alexei A. Efros,et al.  Unsupervised discovery of visual object class hierarchies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  E. Meeds,et al.  Nonparametric Bayesian Methods for Extracting Structure from Data , 2008 .

[17]  Thomas L. Griffiths,et al.  Hierarchical Topic Models and the Nested Chinese Restaurant Process , 2003, NIPS.