A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning

F. Gouidis, A. Vassiliades, T. Patkos, A. Argyros, N. Bassiliades, D. Plexousakis, "A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning", Proc. AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020), Stanford University, Palo Alto, United States, March 2020, CEUR Workshop Proceedings, Vol. 2600.

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Author(s): F. Gouidis, A. Vassiliades, T. Patkos, A. Argyros, N. Bassiliades, D. Plexousakis

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Appeared In: Proc. AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020), Stanford University, Palo Alto, United States, March 2020, CEUR Workshop Proceedings, Vol. 2600.

Keywords: Computer Vision and Pattern Recognition, Artificial Intelligence, Machine Learning, Semantic Web

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2020, Argyros, Bassiliades, Gouidis, Patkos, Plexousakis, Vassiliades

Abstract: Object perception is a fundamental sub-field of Computer Vision, covering a multitude of individual areas and having contributed high-impact results. While Machine Learning has been traditionally applied to address related problems, recent works also seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects, their properties and their relations with their environment. In this paper, we attempt a systematic investigation of how knowledge-based methods contribute to diverse object perception tasks. We review the latest achievements and identify prominent research directions.