Abstract

While navigating in an environment, a vision system has to be able to recognize where it is and what the main objects in the scene are. In this paper we present a context-based vision system for place and object recognition. The goal is to identify familiar locations (e.g., office 610, conference room 941, Main Street), to categorize new environments (office, corridor, street) and to use that information to provide contextual priors for object recognition (e.g., table, chair, car, computer). We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and how such contextual information introduces strong priors that simplify object recognition. We have trained the system to recognize over 60 locations (indoors and outdoors) and to suggest the presence and locations of more than 20 different object types. The algorithm has been integrated into a mobile system that provides real-time feedback to the user.

Keywords

CategorizationComputer scienceCognitive neuroscience of visual object recognitionObject (grammar)Context (archaeology)Artificial intelligenceComputer visionRepresentation (politics)3D single-object recognitionObject detectionSpatial contextual awarenessHuman–computer interactionPattern recognition (psychology)Geography

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Publication Info

Year
2003
Type
article
Pages
273-280 vol.1
Citations
880
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Torralba, Murphy Murphy, Freeman et al. (2003). Context-based vision system for place and object recognition. , 273-280 vol.1. https://doi.org/10.1109/iccv.2003.1238354

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DOI
10.1109/iccv.2003.1238354