Novelty Detection Using Graphical Models for Semantic Room Classification

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

pinto, as (Autor) Outra; pronobis, a (Autor) Outra; reis, lp (Autor) FEUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE

Páginas: 326-339

15th Portuguese Conference on Artificial Intelligence (EPIA 2011)

Lisbon, PORTUGAL, OCT 10-13, 2011

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 3 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-002-VYF

DOI: 10.1007/978-3-642-24769-9_24

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14