Generic object recognition with boosting
Andreas Opelt, Michael Fussenegger, Axel Pinz and Peter Auer
(2004) Technical Report. Graz University of Technology, Graz, Austria.

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Abstract

This paper presents a powerful framework for generic object recognition. Boosting is used as an underlying learning technique. For the first time a combination of various weak classifiers of different types of descriptors is used, which slightly increases the classification result but dramatically improves the stability of a classifier. Besides applying well known techniques to extract salient regions we also present a new segmentation method - "Similarity-Measure-Segmentation". This approach delivers segments, which can consist of several disconnected parts. This turns out to be a mighty description of local similarity. With regard to the task of object categorization, Similarity-Measure-Segmentation performs equal or better than current state-of-the-art segmentation techniques. In contrast to previous solutions we aim at handling of complex objects appearing in highly cluttered images. Therefore we have set up a database containing images with the required complexity. On these images we obtain very good classification results of up to 87% ROC-equal error rate. Focusing the performance on common databases for object recognition our approach outperforms all comparable solutions.

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Available Versions of this Item

• Weak hypotheses and boosting for generic object detection and recognition (deposited 25 November 2004)
  • Generic object recognition with boosting (deposited 29 December 2004) [Currently Displayed]
    • Generic object recognition with boosting (deposited 29 December 2005)

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