Hierarchical Object Indexing and Sequential Learning

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Hierarchical Object Indexing and Sequential Learning
This work is about scene interpretation in the sense of detecting and localizing instances from multiple object classes. We concentrate on object indexing: generate an over-complete interpretation ? a list with extra detections but none missed. Pruning such an index to a final interpretation involves a global, often intensive, contextual analysis. We propose a tree-structured hierarchy as a framework for indexing; each node represents a subset of interpretations. This unifies object representation, scene parsing, and sequential learning (modifying the hierarchy as new samples, poses and classes are encountered). Then we specialize to learning - designing and refining a binary classifier at each node of the hierarchy dedicated to the corresponding subset of interpretations. The whole procedure is illustrated by experiments in reading license plates.
Donald Geman, Xiaodong Fan
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Donald Geman, Xiaodong Fan
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