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Indexing based on scale invariant interest points (2001)
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| Venue: | In Proceedings of the 8th International Conference on Computer Vision |
| Citations: | 408 - 32 self |
BibTeX
@INPROCEEDINGS{Mikolajczyk01indexingbased,
author = {Krystian Mikolajczyk and Cordelia Schmid},
title = {Indexing based on scale invariant interest points},
booktitle = {In Proceedings of the 8th International Conference on Computer Vision},
year = {2001},
pages = {525--531}
}
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Abstract
This paper presents a new method for detecting scale invariant interest points. The method is based on two recent results on scale space: 1) Interest points can be adapted to scale and give repeatable results (geometrically stable). 2) Local extrema over scale of normalized derivatives indicate the presence of characteristic local structures. Our method first computes a multi-scale representation for the Harris interest point detector. We then select points at which a local measure (the Laplacian) is maximal over scales. This allows a selection of distinctive points for which the characteristic scale is known. These points are invariant to scale, rotation and translation as well as robust to illumination changes and limited changes of viewpoint. For indexing, the image is characterized by a set of scale invariant points; the scale associated with each point allows the computation of a scale invariant descriptor. Our descriptors are, in addition, invariant to image rotation, to affine illumination changes and robust to small perspective deformations. Experimental results for indexing show an excellent performance up to a scale factor of 4 for a database with more than 5000 images. 1
Keyphrases
scale invariant interest point illumination change new method scale space distinctive point harris interest point detector scale factor image rotation normalized derivative characteristic scale limited change multi-scale representation local measure local extremum repeatable result characteristic local structure recent result small perspective deformation scale invariant point experimental result scale invariant descriptor excellent performance
