Object Templates for Visual Place Categorization

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.