Joint object-material category segmentation from audio-visual cues

Conference item

It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials. In this paper, we therefore present an approach that augments the available dense visual cues with sparse auditory cues in order to estimate dense object and material labels. Since estimates of object class and material properties are mutually-informative, we optimise our multi-output labelling jointly using a random-field framework. We evaluate our system on a new dataset with paired visual and auditory data that we make publicly available. We demonstrate that this joint estimation of object and material labels significantly outperforms the estimation of either category in isolation.

Authors: Arnab, A; Sapienza, M; Golodetz, S; Valentin, J; Miksik, O

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BMVA Press
• Host title: BMVC 2015: 26th British Machine Vision Conference
• Journal: Proceedings of BMVC 2015
• Volume: abs/1601.02220
• Publication date: 2015-09-10
• DOI: 10.5244/C.29.40
• Keywords: cs.CV; cs.SD