Scalable near duplicate image search with geometric constraints
First Claim
Patent Images
1. A method of processing an image to enable indexing and comparison of the image against other similarly processed images, the method comprising the steps of:
- (a) identifying local regions of interest within the image by a first feature detection scheme, where by the first feature detection scheme comprises the step of identifying local regions of interest within the image by (MSER) maximally stable extremal regions;
(b) identifying local points of interest within the image by a second feature detection scheme;
(c) defining bundled features by grouping together the local points of interest that occur within a given local region of interest; and
(d) comparing bundled features of a first image to bundled features of a second image to determine if the first and second images are at least partial matches of each other.
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Abstract
Methods are disclosed for finding images from a large corpus of images that at least partially match a query image. The present method makes use of feature detectors to bundle features into local groups or bundles. These bundled features are repeatable and much more discriminative than an individual SIFT feature. Equally importantly, the bundled features provide a flexible representation that allows simple and robust geometric constraints to be efficiently enforced when querying the index.
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Citations
8 Claims
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1. A method of processing an image to enable indexing and comparison of the image against other similarly processed images, the method comprising the steps of:
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(a) identifying local regions of interest within the image by a first feature detection scheme, where by the first feature detection scheme comprises the step of identifying local regions of interest within the image by (MSER) maximally stable extremal regions; (b) identifying local points of interest within the image by a second feature detection scheme; (c) defining bundled features by grouping together the local points of interest that occur within a given local region of interest; and (d) comparing bundled features of a first image to bundled features of a second image to determine if the first and second images are at least partial matches of each other. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification