Towards recognizing "cool": can end users help computer vision recognize subjective attributes of objects in images?

Authors:
William Curran;Travis Moore;Todd Kulesza;Weng-Keen Wong;Sinisa Todorovic;Simone Stumpf;Rachel White;Margaret Burnett
Affiliations:
Oregon State University, Corvallis, Oregon, United States;Oregon State University, Corvallis, Oregon, United States;Oregon State University, Corvallis, Oregon, United States;Oregon State University, Corvallis, Oregon, United States;Oregon State University, Corvallis, Oregon, United States;City University London, London, United Kingdom;Oregon State University, Corvallis, Oregon, United States;Oregon State University, Corvallis, Oregon, United States
Venue:
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Year:
2012

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Abstract

Recent computer vision approaches are aimed at richer image interpretations that extend the standard recognition of objects in images (e.g., cars) to also recognize object attributes (e.g., cylindrical, has-stripes, wet). However, the more idiosyncratic and abstract the notion of an object attribute (e.g., cool car), the more challenging the task of attribute recognition. This paper considers whether end users can help vision algorithms recognize highly idiosyncratic attributes, referred to here as subjective attributes. We empirically investigated how end users recognized three subjective attributes of carscool, cute, and classic. Our results suggest the feasibility of vision algorithms recognizing subjective attributes of objects, but an interactive approach beyond standard supervised learning from labeled training examples is needed.