Novelty detection using graphical models for semantic room classification
2011 (English)In: 15th Portuguese Conference on Artificial Intelligence, EPIA 2011, 2011, 326-339 p.Conference paper (Refereed)
This paper presents an approach to the problem of novelty detection in the context of semantic room categorization. The ability to assign semantic labels to areas in the environment is crucial for autonomous agents aiming to perform complex human-like tasks and human interaction. However, in order to be robust and naturally learn the semantics from the human user, the agent must be able to identify gaps in its own knowledge. To this end, we propose a method based on graphical models to identify novel input which does not match any of the previously learnt semantic descriptions. The method employs a novelty threshold defined in terms of conditional and unconditional probabilities. The novelty threshold is then optimized using an unconditional probability density model trained from unlabelled data.
Place, publisher, year, edition, pages
2011. 326-339 p.
, Lecture Notes in Computer Science, ISSN 03029743 ; 7026
Novelty detection – semantic data – probabilistic graphical models – room classification – indoor environments – robotics – multi-modal classification
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-67341DOI: 10.1007/978-3-642-24769-9_24ScopusID: 2-s2.0-80054796335OAI: oai:DiVA.org:kth-67341DiVA: diva2:484896
15th Portuguese Conference on Artificial Intelligence (EPIA'11), Lisbon, Portugal. 10 October 2011 - 13 October 2011
QC 201201302012-01-272012-01-272012-01-30Bibliographically approved