Scalable near duplicate image search with geometric constraints
AssigneeMicrosoft Corporation
InventorsMichael Isard, Qifa Ke, Jian Sun, Zhong Wu
Life of this patent
Estimating the term…
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.
Claims18
A method of finding candidate images from a large corpus of images that at least partially match a query image, the method comprising the steps of:
- (a) identifying local regions of interest within the candidate and query images by a first feature detection scheme;
- (b) identifying local points of interest within the candidate and query images by a second feature detection scheme different than the first;
- (c) defining bundled features within the candidate and query images by grouping together the local points of interest that occur within a given local region of interest;
- (d) comparing bundled features of the query image to bundled features of a candidate image to find a bundled feature in the query image and a bundled feature in the candidate image that share at least two common local points of interest;
- (e) determining the relative positions of local points of interest along first and second perpendicular axes within bundles in the query image and candidate image;
- and
- (f) comparing the relative positions determined in said step (e) between bundles in the first image and the query image.
A computer storage medium not including modulated data signals, the computer storage medium having computer-executable instructions for programming a processor to perform a method of finding candidate images from a large corpus of images that at least partially match a query image, the method comprising the steps of:
- (a) identifying local regions of interest within the candidate and query images by a first feature detection scheme;
- (b) identifying local points of interest within the candidate and query images by a second feature detection scheme different than the first;
- (c) defining bundled features within the candidate and query images by grouping together the local points of interest that occur within a given local region of interest;
- (d) determining feature descriptors for the local points of interest in the query and candidate images;
- (e) processing visual words based on the feature descriptors determined in said step (d);
- and
- (f) comparing one or more visual words from the query image to one or more visual words of a candidate image to determine if the candidate image is at least a partial match to the query image.
A method of finding candidate images from a large corpus of images that at least partially match a query image, the method comprising the steps of:
- (a) identifying local regions of interest within the candidate and query images by a first feature detection scheme;
- (b) identifying local points of interest within the candidate and query images by a second feature detection scheme, different than the first feature detection scheme, the second feature detection scheme comprising a scale-invariant feature transform feature detection scheme;
- (c) defining bundled features within the candidate and query images by grouping together the local points of interest that occur within a given local region of interest;
- and
- (d) comparing bundled features of the query image to bundled features of a candidate image to find a bundled feature in the query image and a bundled feature in the candidate image that share at least two common local points of interest.
Dependencies are inferred from the claim text.