Multiple-Cue Object Recognition on outside datasets

This work builds upon the fact that robots can observe humans interacting with the objects in their environment, and that humans provide numerous non-visual cues to the identity of objects. In previous work, we outlined a Multiple-Cue Object Recognition (MCOR) algorithm which attempted to use multiple features of any type to produce more robust object recognition. All results so far reported with MCOR have been on data collected by ourselves. In this work, we introduce new advancements in the MCOR algorithm to increase its effectiveness and ability to deal with complex real data from outside datasets. These advancements include the integration of Scale-Invariant Feature Transform (SIFT) features and an improvement in training. To demonstrate the effectiveness of the MCOR framework, we first show a comparison of the MCOR algorithm to an outside dataset to show its basic advantages. We then demonstrate the advanced MCOR features on real television video datasets in particular cooking.

[1]  Diane J. Cook,et al.  Learning Membership Functions in a Function-Based Object Recognition System , 1995, J. Artif. Intell. Res..

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

[3]  Irfan A. Essa,et al.  Exploiting human actions and object context for recognition tasks , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[5]  Manuela M. Veloso,et al.  Towards using multiple cues for robust object recognition , 2007, AAMAS '07.

[6]  Dan Roth,et al.  Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Manuela M. Veloso,et al.  Simulation and weights of multiple cues for robust object recognition , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Azriel Rosenfeld,et al.  Recognition by Functional Parts , 1995, Comput. Vis. Image Underst..

[9]  Jochen Triesch,et al.  Shared Features for Scalable Appearance-Based Object Recognition , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[10]  Larry S. Davis,et al.  Objects in Action: An Approach for Combining Action Understanding and Object Perception , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Bernt Schiele,et al.  Functional Object Class Detection Based on Learned Affordance Cues , 2008, ICVS.

[13]  Manuela M. Veloso,et al.  FOCUS: a generalized method for object discovery for robots that observe and interact with humans , 2006, HRI '06.

[14]  Alex Pentland,et al.  Probabilistic visual learning for object detection , 1995, Proceedings of IEEE International Conference on Computer Vision.

[15]  Diane J. Cook,et al.  Learning Fuzzy Membership Functions in a Function-Based Object Recognition System , 1993, Fuzzy Logic in Artificial Intelligence.

[16]  Michael R. Lowry,et al.  Learning Physical Descriptions From Functional Definitions, Examples, and Precedents , 1983, AAAI.

[17]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.