Robust Object Recognition with Cortex-Like Mechanisms

—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. ...

• Type: Journal
• Year: 2007
• Where: PAMI
• Authors: Thomas Serre, Lior Wolf, Stanley M. Bileschi, Maximilian Riesenhuber, Tomaso Poggio
• Keywords: Complex Visual Scenes, Object Recognition, PAMI 2007, Single Object Recognition