Unsupervised feature selection and category formation for mobile robot vision

This paper presents an unsupervised learning-based method for selection of feature points and object category formation without previous setting of the number of categories. For unsupervised object category formation, this method has the following features: detection of feature points and description of features using a Scale-Invariant Feature Transform (SIFT), selection of target feature points using One Class-SVMs (OC-SVMs), generation of visual words using SOMs, formation of labels using ART-2, and creation and classification of categories on a category map of CPNs for visualizing spatial relations between categories. Classification results of static images using a Caltech-256 object category dataset and dynamic images using time-series images obtained using a robot according to movements respectively demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category formation of appearance changes of objects.

[1]  David A. Forsyth,et al.  Matching Words and Pictures , 2003, J. Mach. Learn. Res..

[2]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Long Zhu,et al.  Unsupervised Learning of Probabilistic Grammar-Markov Models for Object Categories , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003 .

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

[6]  Long Zhu,et al.  Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation , 2009, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Stephen Grossberg,et al.  Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.

[8]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[9]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[10]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[11]  Kurita Takio,et al.  Bag-of-features car detection based on selected local features using Support Vector Machine , 2009 .

[12]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[13]  Christoph H. Lampert,et al.  Beyond sliding windows: Object localization by efficient subwindow search , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Narendra Ahuja,et al.  Unsupervised Category Modeling, Recognition, and Segmentation in Images , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[16]  R. Hecht-Nielsen Counterpropagation networks. , 1987, Applied optics.

[17]  Yoshihiro Fujita Personal Robot R100 , 2000 .

[18]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[19]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints Abstract by Matthijs Dorst Based on the paper by , 2011 .