PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient, simultaneous detection of multiple object classes
Philipp Zehnder, Esther Koller-Meier and Luc Van Gool
In: 18th International Conference on Pattern Recognition, 20-24 Aug 2006, Hong Kong, China.


At present, the object categorisation literature is still dominated by the use of individual class detectors. Detecting multiple classes then implies the subsequent application of multiple such detectors, but such an approach is not scalable towards high numbers of classes. This paper presents an alternative strategy, where multiple classes are detected in a combined way. This includes a decision tree approach, where ternary rather than binary nodes are used, and where nodes share features. This yields an efficient scheme, which scales much better. The paper proposes a strategy where the object samples are first distinguished from the background. Then, in a second stage, the actual object class membership of each sample is determined. The focus of the paper lies entirely on the first stage, i.e. the distinction from background. The tree approach for this step is compared against two alternative strategies, one of them being the popular cascade approach. While classification accuracy tends to be better or comparable, the speed of the proposed method is systematically better. This advantage gets more outspoken as the number of object classes increases.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2649
Deposited By:Esther Koller-Meier
Deposited On:22 November 2006