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Robust Object Recognition with Cortex-Like Mechanisms
March 2007 (vol. 29 no. 3)
pp. 411-426
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.
Index Terms:
Object recognition, model, visual cortex, scene understanding, neural network.
Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, Tomaso Poggio, "Robust Object Recognition with Cortex-Like Mechanisms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 411-426, March 2007, doi:10.1109/TPAMI.2007.56
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