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

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 ratic 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 cars—”cool”, “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. Author Keywords Computer vision, interactive machine learning, classification, human factors. ACM Classification Keywords

• Type: Journal
• Year: 2012
• Where: IUI
• Authors: William Curran, Travis Moore, Todd Kulesza, Weng-Keen Wong, Sinisa Todorovic, Simone Stumpf, Rachel White, Margaret M. Burnett
• Keywords: Interactive Machine, IUI 2012, Object Attributes, Software Engineering, Vision Algorithms