Abstract
Traditional approaches to object detection only look at local pieces of the image, whether it be within a sliding window or the regions around an interest point detector. However, such local pieces can be ambiguous, especially when the object of interest is small, or imaging conditions are otherwise unfavorable. This ambiguity can be reduced by using global features of the image — which we call the “gist” of the scene — as an additional source of evidence. We show that by combining local and global features, we get significantly improved detection rates. In addition, since the gist is much cheaper to compute than most local detectors, we can potentially gain a large increase in speed as well.
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Murphy, K., Torralba, A., Eaton, D., Freeman, W. (2006). Object Detection and Localization Using Local and Global Features. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11957959_20
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DOI: https://doi.org/10.1007/11957959_20
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