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Comparison between Bayesian network classifiers and SVMs for semantic localization
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DOI：10.1016/j.eswa.2016.08.029.png)
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Abstract
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This work presents a methodology to apply Bayesian networks classifiers (BNCs) to the problem of semantic localization in robotics. This task consists of determining where the robot is located by using semantic annotations instead of metric locations, and based on robots perceptions, namely images. The proposal covers the two key steps of (1) extracting descriptive features from the input image and (2) construction and evaluation of models, comparing the performance of BNCs technologies with SVMs solutions. The experimentation is performed over two different datasets, and the results, given in terms of accuracy, provide a quite appealing analysis where specialization versus generalization or model complexity are considered. Overall BNCs proved to be quite competitive, and appear to be a very promising tool for future applications since they would allow the introduction of additional contextual information to the processing pipeline. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Semantic localization
Bayesian networks
Robotics
Robot vision
Structural learning
Indoor scene classification
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