Abstract
The calculation of local features at points of interest is a vital part of many current image retrieval and object detection systems. The wavelet-based interest point detector by Loupias et al. was especially developed for image retrieval applications. We show how the detector can be extended by a Laplacian scale selection mechanism to provide scale information and compare it to other state of the art detectors. The extended detector is very well suited for visual object class recognition using feature cluster histograms. It discovers a variety of image structures distributed over the entire image, and the number of regions obtained can be adjusted easily. These properties lead to superior performance, which we confirmed by tests on a difficult animal categorization problem.
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