Object recognition from local scale-invariant features
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238 citations
Cites background or methods from "Object recognition from local scale..."
...As an alternative to the block-based methods, keypointbased forgery detection methods were proposed, where image keypoints are extracted and matched over the whole image to resist some image transformations while identifying duplicated regions....
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...In this way, the detected regions are always composed of regular blocks, which cannot represent the accurate forgery region well; as a consequence, the recall rate of the block-based methods is always very low, for example, as in [8] and [9]....
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238 citations
237 citations
Cites background from "Object recognition from local scale..."
...To address this challenge, we study the problem of tag completion, where the goal is to automatically fill in the missing tags as well as correct noisy tags for given images....
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236 citations
236 citations
Cites methods from "Object recognition from local scale..."
...On the Caltech 101 object recognition benchmark, we achieve 60.3% mean recognition across classes using 15 training images per class, which is better than the best published performance by Zhang, et al....
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...If we haveN training images, we will be solvingN separate learning problems....
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References
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"Object recognition from local scale..." refers background or methods in this paper
...This allows for the use of more distinctive image descriptors than the rotation-invariant ones used by Schmid and Mohr, and the descriptor is further modified to improve its stability to changes in affine projection and illumination....
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...For the object recognition problem, Schmid & Mohr [19] also used the Harris corner detector to identify interest points, and then created a local image descriptor at each interest point from an orientation-invariant vector of derivative-of-Gaussian image measurements....
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..., Schmid & Mohr [19]) has shown that efficient recognition can often be achieved by using local image descriptors sampled at a large number of repeatable locations....
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...However, recent research on the use of dense local features (e.g., Schmid & Mohr [19]) has shown that efficient recognition can often be achieved by using local image descriptors sampled at a large number of repeatable locations....
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1,574 citations
"Object recognition from local scale..." refers methods in this paper
...[23] used the Harris corner detector to identify feature locations for epipolar alignment of images taken from differing viewpoints....
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