What are the visual features underlying rapid object recognition?
- Cognitive, Linguistic, and Psychological Sciences Department, Institute for Brain Sciences, Brown University, Providence, RI, USA
Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without prior segmentation or any explicit encoding of spatial relations. While these models remain simplistic models of visual processing, they suggest that, in principle, bottom-up activation of a loose collection of image features could support the rapid recognition of natural object categories and provide an initial coarse visual representation before more complex visual routines and attentional mechanisms take place. Focusing on biologically plausible computational models of (bottom-up) pre-attentive visual recognition, we review some of the key visual features that have been described in the literature. We discuss the consistency of these feature-based representations with classical theories from visual psychology and test their ability to account for human performance on a rapid object categorization task.
Keywords: rapid visual object recognition, computational models, visual features, computer vision, feedforward
Citation: Crouzet SM and Serre T (2011) What are the visual features underlying rapid object recognition? Front. Psychology 2:326. doi: 10.3389/fpsyg.2011.00326
Received: 17 March 2011;
Accepted: 23 October 2011;
Published online: 15 November 2011.
Edited by:Rufin VanRullen, Centre de Recherche Cerveau et Cognition, France
Copyright: © 2011 Crouzet and Serre. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.
*Correspondence: Sébastien M. Crouzet, Cognitive, Linguistic, and Psychological Sciences Department, Institute for Brain Sciences, Brown University, Providence, RI 02912, USA. e-mail: firstname.lastname@example.org