title: Robust object recognition with cortex-like mechanisms.
abstract: We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex.
authors.affiliation: Massachusetts Institute of Technology, Center for Biological and Computational Learning, McGovern Institute for Brain Research and Brain & Cognitive Sciences Department, MA 02139, USA. serre@mit.edu
authors.forename: Thomas
authors.initials: T
authors.lastname: Serre
authors.forename: Lior
authors.initials: L
authors.lastname: Wolf
authors.forename: Stanley
authors.initials: S
authors.lastname: Bileschi
authors.forename: Maximilian
authors.initials: M
authors.lastname: Riesenhuber
authors.forename: Tomaso
authors.initials: T
authors.lastname: Poggio
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000502:physiology*