Generic object recognition using boosted combined features

Authors:
Doaa Hegazy;Joachim Denzler
Affiliations:
Computer Vision, Institute of Computer Science, Friedrich-Schiller-University, Jena, Germany;Computer Vision, Institute of Computer Science, Friedrich-Schiller-University, Jena, Germany
Venue:
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Year:
2008

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Abstract

In generic object recognition, the performance of local descriptors varies from class category to another. A descriptor may have a good performance on one category and low performance on another. Combining more than one descriptor in recognition can give a solution to this problem. The choice of descriptor's type and number of descriptors to be used is then important. In this paper, we use two different types of descriptors, the Gradient Location-Orientation Histogram (GLOH) and simple color descriptor, for generic object recognition. Boosting is used as the underlying learning technique. The recognition model achieves a performance that is comparable to or better than that of state-of-the-art approaches.