Statistical bigram correlation model for image retrieval
First Claim
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1. A computer-implemented method for image retrieval using a statistical bigram correlation model, the method comprising:
- receiving a plurality of images responsive to multiple search sessions;
determining whether the images are semantically relevant images via relevance feedback;
determining a maximum frequency from a maximum value of bigram and unigram frequencies;
estimating, via the statistical bigram correlation model, a respective semantic correlation between each image of at least one pair of the images with a respective bigram frequency, each respective bigram frequency being based on multiple search sessions in which said each image of the pair is indicated to be a semantically relevant image, said each respective bigram frequency representing a probability of whether two images that are semantically related to one-another based on a co-occurrence frequency, each image of the two images was relevant in a previous query/feedback session,wherein the estimating the respective semantic correlation further comprises;
associating a respective unigram frequency with each respective image of the images, the respective unigram frequency indicating that said each respective image of the images is either semantically relevant, semantically less relevant, or a non-feedback image, the unigram frequency being based on relevance feedback to a session of the respective multiple search sessions, wherein the respective semantic correlation is further based on the maximum frequency; and
displaying, responsive to a user operation in an image retrieval session, one or more ranked images to a user based on the respective semantic correlation.
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Abstract
The disclosed subject matter improves iterative results of content-based image retrieval (CBIR) using a bigram model to correlate relevance feedback. Specifically, multiple images are received responsive to multiple image search sessions. Relevance feedback is used to determine whether the received images are semantically relevant. A respective semantic correlation between each of at least one pair of the images is then estimated using respective bigram frequencies. The bigram frequencies are based on multiple search sessions in which each image of a pair of images is semantically relevant.
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Citations
15 Claims
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1. A computer-implemented method for image retrieval using a statistical bigram correlation model, the method comprising:
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receiving a plurality of images responsive to multiple search sessions; determining whether the images are semantically relevant images via relevance feedback; determining a maximum frequency from a maximum value of bigram and unigram frequencies; estimating, via the statistical bigram correlation model, a respective semantic correlation between each image of at least one pair of the images with a respective bigram frequency, each respective bigram frequency being based on multiple search sessions in which said each image of the pair is indicated to be a semantically relevant image, said each respective bigram frequency representing a probability of whether two images that are semantically related to one-another based on a co-occurrence frequency, each image of the two images was relevant in a previous query/feedback session, wherein the estimating the respective semantic correlation further comprises; associating a respective unigram frequency with each respective image of the images, the respective unigram frequency indicating that said each respective image of the images is either semantically relevant, semantically less relevant, or a non-feedback image, the unigram frequency being based on relevance feedback to a session of the respective multiple search sessions, wherein the respective semantic correlation is further based on the maximum frequency; and displaying, responsive to a user operation in an image retrieval session, one or more ranked images to a user based on the respective semantic correlation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer-readable storage medium for image retrieval using a statistical bigram correlation model, the computer-readable storage medium comprising computer-executable instructions for:
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receiving a plurality of images responsive to multiple image retrieval search sessions; determining whether the images are semantically relevant images via relevance feedback; determining a maximum frequency from a maximum value of bigram and unigram frequencies; estimating, via the statistical bigram correlation model, a respective semantic correlation between each image of at least one pair of the images with a respective bigram frequency, each respective bigram frequency being based on multiple search sessions in which said each image of the pair is indicated to be a semantically relevant image, said each respective bigram frequency representing a probability of whether two images that are semantically related to one-another based on a co-occurrence frequency, each image of the two images was relevant in a previous query/feedback session, wherein the estimating the respective semantic correlation further comprises; associating a respective unigram frequency with each respective image of the images, the respective unigram frequency indicating that said each respective image of the images is either semantically relevant, semantically less relevant, or a non-feedback image, the unigram frequency being based on relevance feedback to a session of the respective multiple search sessions, wherein the respective semantic correlation is further based on the maximum frequency; and displaying, responsive to a user operation in an image retrieval session, one or more ranked images to a user based on the respective semantic correlation. - View Dependent Claims (13, 14)
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15. A computing device for image retrieval using a statistical bigram correlation model, the computing device comprising:
a processor; and
a memory coupled to the processor, the memory comprising computer-executable instructions that are fetched and executed by the processor for;receiving a plurality of images responsive to multiple image retrieval search sessions; determining whether the images are semantically relevant images via relevance feedback; determining a maximum frequency from a maximum value of bigram and unigram frequencies; estimating, via the statistical bigram correlation model, a respective semantic correlation between each image of at least one pair of the images with a respective bigram frequency, each respective bigram frequency being based on multiple search sessions in which said each image of the pair is indicated to be a semantically relevant image, said each respective bigram frequency representing a probability of whether two images that are semantically related to one-another based on a co-occurrence frequency, each image of the two images was relevant in a previous query/feedback session, wherein the estimating the respective semantic correlation further comprises; associating a respective unigram frequency with each respective image of the images, the respective unigram frequency indicating that said each respective image of the images is either semantically relevant, semantically less relevant, or a non-feedback image, the respective unigram frequency being based on relevance feedback to a session of the multiple search sessions, wherein the respective semantic correlation is further based on the maximum frequency; and displaying, responsive to a user operation in an image retrieval session, one or more ranked images to a user based on the respective semantic correlation.
Specification