Towards simultaneous place classification and object detection based on conditional random field with multiple cues

Simultaneous place classification and object detection (SPCOD) is an algorithm which is able to categorize the environment (place) and detect the objects presented in the environment. Although both place classification and object detection problems have been in discussion in literature, as a concept SPCOD is still in its early stage of research. Focusing mainly on the discrimination ability of SPCOD, in this paper we have proposed a pairwise conditional random field (CRF) framework to integrate mature techniques on laser data based place classification and vision based off-the-shelf object descriptor. Extensive experimental results on a public data set demonstrate the effectiveness of the proposed method.

[1]  Mark W. Schmidt,et al.  Accelerated training of conditional random fields with stochastic gradient methods , 2006, ICML.

[2]  Shawn D. Newsam,et al.  Can off-the-shelf object detectors be used to extract geographic information from geo-referenced social multimedia? , 2012, LBSN '12.

[3]  Wolfram Burgard,et al.  From labels to semantics: an integrated system for conceptual spatial representations of indoor environments for mobile robots , 2007 .

[4]  Barbara Caputo,et al.  The more you learn, the less you store: Memory-controlled incremental SVM for visual place recognition , 2010, Image Vis. Comput..

[5]  Ben Taskar,et al.  An Introduction to Conditional Random Fields for Relational Learning , 2007 .

[6]  Barbara Caputo,et al.  COLD: The CoSy Localization Database , 2009, Int. J. Robotics Res..

[7]  Trevor Darrell,et al.  Detection bank: an object detection based video representation for multimedia event recognition , 2012, ACM Multimedia.

[8]  Hao Su,et al.  Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification , 2010, NIPS.

[9]  Jongwoo Lim,et al.  Visual place categorization in maps , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  U. Nunes,et al.  Real-Time Labeling of Places using Support Vector Machines , 2007, 2007 IEEE International Symposium on Industrial Electronics.

[11]  James M. Rehg,et al.  Visual Place Categorization: Problem, dataset, and algorithm , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Lei Shi,et al.  Multi-class classification for semantic labeling of places , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[13]  Antonio Torralba,et al.  Context-based vision system for place and object recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[14]  Huaqing Min,et al.  Simultaneous place and object recognition with mobile robot using pose encoded contextual information , 2011, 2011 IEEE International Conference on Robotics and Automation.

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

[16]  Lei Shi,et al.  Laser range data based semantic labeling of places , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  B. Hjorth.  EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[18]  Lei Shi,et al.  Towards generalization of semi-supervised place classification over generalized Voronoi graph , 2013, Robotics Auton. Syst..

[19]  Dieter Fox,et al.  Voronoi Random Fields: Extracting Topological Structure of Indoor Environments via Place Labeling , 2007, IJCAI.

[20]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[21]  In-So Kweon,et al.  Simultaneous place and object recognition using collaborative context information , 2009, Image Vis. Comput..

[22]  Roland Siegwart,et al.  Bayesian space conceptualization and place classification for semantic maps in mobile robotics , 2008, Robotics Auton. Syst..

[23]  A. Poncela,et al.  Place characterization for navigation via behaviour merging for an autonomous mobile robot , 2008, MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference.

[24]  Mark W. Schmidt,et al.  Structure learning in random fields for heart motion abnormality detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Wolfram Burgard,et al.  Supervised Learning of Places from Range Data using AdaBoost , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.