Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
COLD: The CoSy Localization Database
International Journal of Robotics Research
Visual place categorization: problem, dataset, and algorithm
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Multi-modal Semantic Place Classification
International Journal of Robotics Research
CENTRIST: A Visual Descriptor for Scene Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Place Classification Using Visual Object Categorization and Global Information
CRV '11 Proceedings of the 2011 Canadian Conference on Computer and Robot Vision
Balance support vector machines locally using the structural similarity kernel
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Efficient and Effective Visual Codebook Generation Using Additive Kernels
The Journal of Machine Learning Research
Similarity scores based on background samples
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Histogram of Oriented Uniform Patterns for robust place recognition and categorization
International Journal of Robotics Research
PLISS: labeling places using online changepoint detection
Autonomous Robots
Power mean SVM for large scale visual classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Scene recognition and weakly supervised object localization with deformable part-based models
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The Visual Place Categorization (VPC) problem refers to the categorization of the semantic category of a place using only visual information collected from an autonomous robot. Previous works on this problem only made use of the global configurations observation, such as the Bag-of-Words model and spatial pyramid matching. In this paper, we present a novel system solving the problem utilizing both global configurations observation and local objects information. To be specific, we propose a local objects classifier that can automatically and effectively select key local objects of a semantic category from randomly sampled patches by the structural similarity support vector machine; and further classify the test frames with the Local Naive Bayes Nearest Neighbors algorithm. We also improve the global configurations observation with histogram intersection codebook and a noisy codewords removal mechanism. The temporal smoothness of the classification results is ensured by employing a Bayesian filtering framework. Empirically, our system outperforms state-of-the-art methods on two large scale and difficult datasets, demonstrating the superiority of the system.