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. © 2013 Springer-Verlag.
CITATION STYLE
Yang, H., & Wu, J. (2013). Object templates for visual place categorization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7727 LNCS, pp. 470–483). https://doi.org/10.1007/978-3-642-37447-0_36
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