Comparison between Bayesian network classifiers and SVMs for semantic localization

Fernando Rubio Perona, Jesus Martínez-Gómez, M. Julia Flores, Jose M. Puerta

Abstract:

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.