HOP: Hierarchical object parsing

In this paper we consider the problem of object parsing, namely detecting an object and its components by composing them from image observations. Apart from object localization, this involves the question of combining top-down (model-based) with bottom-up (image-based) information. We use an hierarchical object model, that recursively decomposes an object into simple structures. Our first contribution is the formulation of composition rules to build the object structures, while addressing problems such as contour fragmentation and missing parts. Our second contribution is an efficient inference method for object parsing that addresses the combinatorial complexity of the problem. For this we exploit our hierarchical object representation to efficiently compute a coarse solution to the problem, which we then use to guide search at a finer level. This rules out a large portion of futile compositions and allows us to parse complex objects in heavily cluttered scenes.

[1]  B. Schiele,et al.  Combined Object Categorization and Segmentation With an Implicit Shape Model , 2004 .

[2]  Pietro Perona,et al.  Recognition by Probabilistic Hypothesis Construction , 2004, ECCV.

[3]  David A. McAllester,et al.  The Generalized A* Architecture , 2007, J. Artif. Intell. Res..

[4]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[5]  Jianbo Shi,et al.  Contour Context Selection for Object Detection: A Set-to-Set Contour Matching Approach , 2008, ECCV.

[6]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Zhuowen Tu,et al.  Image Parsing: Unifying Segmentation, Detection, and Recognition , 2005, International Journal of Computer Vision.

[8]  Andrew Zisserman,et al.  A Boundary-Fragment-Model for Object Detection , 2006, ECCV.

[9]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.

[10]  M. Zeldin.  Heuristics! , 2010 .

[11]  Feng Han,et al.  Bottom-up/top-down image parsing by attribute graph grammar , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[12]  Stuart Geman,et al.  Context and Hierarchy in a Probabilistic Image Model , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[14]  Sanja Fidler,et al.  Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Van Nam Tran,et al.  Syntactic pattern recognition , 1978 .

[16]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[17]  Joshua D. Schwartz,et al.  Hierarchical Matching of Deformable Shapes , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Christoph H. Lampert,et al.  Beyond sliding windows: Object localization by efficient subwindow search , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[20]  Christopher K. I. Williams,et al.  Image Modeling with Position-Encoding Dynamic Trees , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Long Zhu,et al.  Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing , 2007, NIPS.

[22]  Long Zhu,et al.  Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion , 2008, ECCV.

[23]  Iasonas Kokkinos,et al.  Unsupervised Learning of Object Deformation Models , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[24]  Joachim M. Buhmann,et al.  Learning the Compositional Nature of Visual Objects , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Frédéric Jurie,et al.  Groups of Adjacent Contour Segments for Object Detection , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Dan Roth,et al.  Learning a Sparse Representation for Object Detection , 2002, ECCV.

[27]  Luc Van Gool,et al.  Object Detection by Contour Segment Networks , 2006, ECCV.

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