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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Object Detection and Localization Using Local and Global Features
verfasst von : Kevin Murphy, Antonio Torralba, Daniel Eaton, William Freeman
Erschienen in: Toward Category-Level Object Recognition
Verlag: Springer Berlin Heidelberg
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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.