Scalable image matching
First Claim
1. A computer-implemented method, comprising:
- matching a plurality of training images against a database of images using one or more training features associated with the plurality of training images;
receiving one or more matching images from the database of images, wherein each matching image includes a plurality of correspondences with the one or more training features from the plurality of training images;
labeling a first subset of the plurality of correspondences as inlier correspondences and a second subset of the plurality of correspondences as outlier correspondences;
determining a first set of characteristics of the first subset of the plurality of correspondences;
determining a second set of characteristics of the second subset of the plurality of correspondences;
training one or more classifiers using the first set of characteristics and the second set of characteristics to generate a prediction model, wherein the prediction model is used at runtime to determine matching scores for the database of images in response to a query image;
ranking the database of images by comparing features from the query image to a set of compressed cluster centers corresponding to a set of closest matching database images; and
adjusting the ranking of the set of closest matching database images using the prediction model, wherein adjusting the ranking of the set of closest matching database images includes determining whether a respective compressed cluster center is an inlier or an outlier using the one or more classifiers.
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Abstract
Various embodiments may increase scalability of image representations stored in a database for use in image matching and retrieval. For example, a system providing image matching can obtain images of a number of inventory items, extract features from each image using a feature extraction algorithm, and transform the same into their feature descriptor representations. These feature descriptor representations can be subsequently stored and used to compare against query images submitted by users. Though the size of each feature descriptor representation isn'"'"'t particularly large, the total number of these descriptors requires a substantial amount of storage space. Accordingly, feature descriptor representations are compressed to minimize storage and, in one example, machine learning can be used to compensate for information lost as a result of the compression.
10 Citations
17 Claims
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1. A computer-implemented method, comprising:
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matching a plurality of training images against a database of images using one or more training features associated with the plurality of training images; receiving one or more matching images from the database of images, wherein each matching image includes a plurality of correspondences with the one or more training features from the plurality of training images; labeling a first subset of the plurality of correspondences as inlier correspondences and a second subset of the plurality of correspondences as outlier correspondences; determining a first set of characteristics of the first subset of the plurality of correspondences; determining a second set of characteristics of the second subset of the plurality of correspondences; training one or more classifiers using the first set of characteristics and the second set of characteristics to generate a prediction model, wherein the prediction model is used at runtime to determine matching scores for the database of images in response to a query image; ranking the database of images by comparing features from the query image to a set of compressed cluster centers corresponding to a set of closest matching database images; and adjusting the ranking of the set of closest matching database images using the prediction model, wherein adjusting the ranking of the set of closest matching database images includes determining whether a respective compressed cluster center is an inlier or an outlier using the one or more classifiers. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computing system, comprising:
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a processor; and memory including instructions that, when executed by the processor, cause the computing system to; match a plurality of training images against a database of images using one or more training features associated with the plurality of training images; receive one or more matching images from the database of images, wherein each matching image includes a plurality of correspondences with the one or more training features from the plurality of training images; label a first subset of the plurality of correspondences as inlier correspondences and a second subset of the plurality of correspondences as outlier correspondences; determine a first set of characteristics of the first subset of the plurality of correspondences; determine a second set of characteristics of the second subset of the plurality of correspondences; and train one or more classifiers using the first set of characteristics and the second set of characteristics to generate a prediction model, wherein the prediction model is used at runtime to determine matching scores for the database of images in response to a query image; rank the database of images by comparing features from the query image to a set of compressed cluster centers corresponding to a set of closest matching database images; and adjust the ranking of the set of closest matching database images using the prediction model, wherein adjusting the ranking of the set of closest matching database images includes determining whether a respective compressed cluster center is an inlier or an outlier using the one or more classifiers. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A non-transitory computer readable storage medium including instructions stored thereon which, when executed by a processor, cause the processor to:
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match a plurality of training images against a database of images using one or more training features associated with the plurality of training images; receive one or more matching images from the database of images, wherein each matching image includes a plurality of correspondences with the one or more training features from the plurality of training images; label a first subset of the plurality of correspondences as inlier correspondences and a second subset of the plurality of correspondences as outlier correspondences; determine a first set of characteristics of the first subset of the plurality of correspondences; determine a second set of characteristics of the second subset of the plurality of correspondences; and train one or more classifiers using the first set of characteristics and the second set of characteristics to generate a prediction model, wherein the prediction model is used at runtime to determine matching scores for the database of images in response to a query image; rank the database of images by comparing features from the query image to a set of compressed cluster centers corresponding to a set of closest matching database images; and adjust the ranking of the set of closest matching database images using the prediction model, wherein adjusting the ranking of the set of closest matching database images includes determining whether a respective compressed cluster center is an inlier or an outlier using the one or more classifiers. - View Dependent Claims (14, 15, 16, 17)
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Specification