Scenes vs. objects: A comparative study of two approaches to context based recognition

Contextual models play a very important role in the task of object recognition. Over the years, two kinds of contextual models have emerged: models with contextual inference based on the statistical summary of the scene (we will refer to these as scene based context models, or SBC), and models representing the context in terms of relationships among objects in the image (object based context, or OBC). In designing object recognition systems, it is necessary to understand the theoretical and practical properties of such approaches. This work provides an analysis of these models and evaluates two of their representatives using the LabelMe dataset. We demonstrate a considerable margin of improvement using the OBC style approach.

[1]  Jitendra Malik,et al.  Shape matching and object recognition using low distortion correspondences , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  Thomas M. Strat,et al.  Recognizing objects in a natural environment: a contextual vision system (CVS) , 1989 .

[3]  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.

[4]  Antonio Torralba,et al.  Contextual Priming for Object Detection , 2003, International Journal of Computer Vision.

[5]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[6]  R. Fergus,et al.  Tiny images , 2007 .

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

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

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

[10]  Thomas M. Strat,et al.  Natural Object Recognition , 1992, Springer Series in Perception Engineering.

[11]  Antonio Torralba,et al.  Object Recognition by Scene Alignment , 2007, NIPS.

[12]  Lior Wolf,et al.  A Critical View of Context , 2006, International Journal of Computer Vision.

[13]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[14]  Jun Zhang,et al.  A Markov random field model-based approach to image interpretation , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[16]  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..

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

[18]  Bill Triggs,et al.  Scene Segmentation with CRFs Learned from Partially Labeled Images , 2007, NIPS.