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Hierarchical object indexing and sequential learning

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2 Author(s)
Xiaodong Fan ; Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA ; D. Geman

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.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:3 )

Date of Conference:

23-26 Aug. 2004