Year-end Sale % OFF 
Abstract
This article presents a new method for supervised image classification. Given a finite number of image sets, each set corresponding to a place of an environment, we propose a localization strategy, which relies upon supervised classification. For each place, the corresponding landmark is actually a combination of features that have to be detected in the image set. Moreover, these features are extracted using a symbolic knowledge extraction theory, formal concept analysis. This paper details the full landmark extraction process and its hierarchical organization. A real localization problem in a structured environment is processed as an illustration. This approach is compared with an optimized neural network-based classification, and validated with experimental results. Further research to build up hybrid classifier is outlined in the discussion.
Highlights
Characterizing and recognizing a place in a structured or not environment, using only a set of views attached to each place to characterize, is a difficult challenge to take up for a machine today
Given a finite number of image sets, each set corresponding to a place of an environment, we propose a localization strategy, which relies upon supervised classification
Our objective is to provide a mobile robot, equipped with a camera, with a decision rule to allow it to find its localization in a topological map
Summary
Characterizing and recognizing a place in a structured or not environment, using only a set of views attached to each place to characterize, is a difficult challenge to take up for a machine (computer or robot) today. At first, during a learning stage, the relationships between sets of images and features are structured and organized into a hierarchy, through a formalism called Galois lattices, or concept lattices. The use of such mathematical structures allows the machine to determine its own landmarks attached to each place. Once this initial characterization has been performed, the machine is able in a second stage to recognize the corresponding place thanks to the landmarks it has learned
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
