Context by region ancestry

In this paper, we introduce a new approach for modeling visual context. For this purpose, we consider the leaves of a hierarchical segmentation tree as elementary units. Each leaf is described by features of its ancestral set, the regions on the path linking the leaf to the root. We construct region trees by using a high-performance segmentation method. We then learn the importance of different descriptors (e.g. color, texture, shape) of the ancestors for classification. We report competitive results on the MSRC segmentation dataset and the MIT scene dataset, showing that region ancestry efficiently encodes information about discriminative parts, objects and scenes.

[1]  tephen E. Palmer The effects of contextual scenes on the identification of objects , 1975, Memory & cognition.

[2]  I. Biederman,et al.  Scene perception: Detecting and judging objects undergoing relational violations , 1982, Cognitive Psychology.

[3]  Thomas M. Strat,et al.  Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  J. Henderson,et al.  High-level scene perception. , 1999, Annual review of psychology.

[5]  Antonio Torralba,et al.  Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes , 2003, NIPS.

[6]  Miguel Á. Carreira-Perpiñán,et al.  Multiscale conditional random fields for image labeling , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[8]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[9]  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).

[10]  Martial Hebert,et al.  A hierarchical field framework for unified context-based classification , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[11]  Alexei A. Efros,et al.  Geometric context from a single image , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[12]  Antonio Torralba,et al.  Depth from Familiar Objects: A Hierarchical Model for 3D Scenes , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  Jitendra Malik,et al.  Image Retrieval and Classification Using Local Distance Functions , 2006, NIPS.

[14]  Antonio Criminisi,et al.  TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation , 2006, ECCV.

[15]  Bill Triggs,et al.  Region Classification with Markov Field Aspect Models , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

[17]  Cordelia Schmid,et al.  Object Recognition by Integrating Multiple Image Segmentations , 2008, ECCV.

[18]  Long Zhu,et al.  Recursive Segmentation and Recognition Templates for 2D Parsing , 2008, NIPS.

[19]  Tsuhan Chen,et al.  From appearance to context-based recognition: Dense labeling in small images , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[21]  Daphne Koller,et al.  Learning Spatial Context: Using Stuff to Find Things , 2008, ECCV.

[22]  Jitendra Malik,et al.  Using contours to detect and localize junctions in natural images , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Stephen Gould,et al.  Multi-Class Segmentation with Relative Location Prior , 2008, International Journal of Computer Vision.

[24]  Pushmeet Kohli,et al.  Robust Higher Order Potentials for Enforcing Label Consistency , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Narendra Ahuja,et al.  Learning subcategory relevances for category recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Jason J. Corso Discriminative modeling by Boosting on Multilevel Aggregates , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Zhuowen Tu,et al.  Auto-context and its application to high-level vision tasks , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Roberto Cipolla,et al.  Semantic texton forests for image categorization and segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Tsuhan Chen,et al.  Learning class-specific affinities for image labelling , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Jitendra Malik,et al.  From contours to regions: An empirical evaluation , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Jitendra Malik,et al.  From contours to regions: An empirical evaluation , 2009, CVPR.