Bayesian active object recognition via Gaussian process regression

This paper is concerned with a Bayesian approach of actively selecting camera parameters in order to recognize a given object from a finite set of object classes. Gaussian process regression is applied to learn the likelihood of image features given the object classes and camera parameters. In doing so, the object recognition task can be treated as Bayesian state estimation problem. For improving the recognition accuracy and speed, the selection of appropriate camera parameters is formulated as a sequential optimization problem. Mutual information is considered as optimization criterion, which aims at maximizing the information from camera observations or equivalently at minimizing the uncertainty of the state estimate.

[1]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[2]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[3]  Joachim Denzler,et al.  Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Subhashis Banerjee,et al.  Active recognition through next view planning: a survey , 2004, Pattern Recognit..

[5]  Lucas Paletta,et al.  Active object recognition by view integration and reinforcement learning , 2000, Robotics Auton. Syst..

[6]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[7]  Lucas Paletta,et al.  Appearance-based active object recognition , 2000, Image Vis. Comput..

[8]  Uwe D. Hanebeck,et al.  Analytic moment-based Gaussian process filtering , 2009, ICML '09.

[9]  Marco F. Huber.  Probabilistic Framework for Sensor Management , 2009 .

[10]  Kaare Brandt Petersen,et al.  The Matrix Cookbook , 2006 .

[11]  Marcus R. Frean,et al.  Dependent Gaussian Processes , 2004, NIPS.

[12]  Trevor Darrell,et al.  Discriminative Gaussian process latent variable model for classification , 2007, ICML '07.

[13]  Tal Arbel,et al.  Efficient Discriminant Viewpoint Selection for Active Bayesian Recognition , 2006, International Journal of Computer Vision.

[14]  Hugh F. Durrant-Whyte,et al.  On entropy approximation for Gaussian mixture random vectors , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[15]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[16]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[17]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[18]  Joachim Denzler,et al.  Viewpoint Selection - Planning Optimal Sequences of Views for Object Recognition , 2003, CAIP.

[19]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[20]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[21]  Guido C. H. E. de Croon,et al.  Comparing active vision models , 2009, Image Vis. Comput..

[22]  Roger Fletcher,et al.  Practical methods of optimization; (2nd ed.) , 1987 .

[23]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[24]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[25]  Shiri Gordon,et al.  An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.