Abstract
We examine the use of complementary descriptors for keypoint recognition in digital images. The descriptors combine multiple types of information, including shape, color, and texture. We first review several keypoint descriptors and propose new descriptors that use normalized brightness/color spatial histograms. Individual and combined descriptors are compared on a standard data set that varies blur, viewpoint, zoom, rotation, brightness, and compression. Results indicate that substantially improved results can be achieved without greatly increasing keypoint descriptor length, but that the best results combine information from complementary descriptors.
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Notes
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Note that this is the SURF keypoint detector, not the descriptor, which has not performed well in our experiments [11].
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Acknowledgment
This work was supported, in part, by a Worthington Distinguished Scholar award from the University of Washington Bothell.
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Olson, C.F., Hoover, S.A., Soltman, J.L., Zhang, S. (2016). Complementary Keypoint Descriptors. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_32
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