Statistical bigram correlation model for image retrieval
First Claim
Patent Images
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;
estimating a respective semantic correlation between each 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 each image of the pair is indicated to be a semantically relevant image;
wherein the respective semantic correlation is performed offline or online to calculate unigram and bigram frequencies from relevance feedback information, the unigram frequency being based on relevance feedback to a session of the multiple search sessions, the unigram frequency indicating that each respective image of the images is either semantically relevant to the session, semantically less relevant to the session, or a non-feedback image with respect to the session; and
wherein each respective bigram frequency is based on a pair of unigram frequencies.
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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.
74 Citations
52 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;
estimating a respective semantic correlation between each 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 each image of the pair is indicated to be a semantically relevant image;
wherein the respective semantic correlation is performed offline or online to calculate unigram and bigram frequencies from relevance feedback information, the unigram frequency being based on relevance feedback to a session of the multiple search sessions, the unigram frequency indicating that each respective image of the images is either semantically relevant to the session, semantically less relevant to the session, or a non-feedback image with respect to the session; and
wherein each respective bigram frequency is based on a pair of unigram frequencies. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A computer-readable medium for image retrieval using a statistical bigram correlation model, the computer-readable medium comprising computer-executable instructions for:
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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;
estimating a respective semantic correlation between each of at least one pair of the images with a respective bigram frequency, each respective bigram frequency representing a probability of whether two of the images are semantically related to one-another based on a co-occurrence frequency that each image of the two images was relevant in a previous query/feedback session;
wherein the respective semantic correlation is performed offline or online to calculate unigram and bigram frequencies from relevance feedback information, the unigram frequency being based on relevance feedback to a session of the multiple search sessions, the unigram frequency indicating that each respective image of the images is either semantically relevant to the session, semantically less relevant to the session, or a non-feedback image with respect to the session; and
wherein each respective bigram frequency is based on a pair of unigram frequencies. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
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27. A computing device for image retrieval using a statistical bigram correlation model, the computing device comprising:
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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 search sessions;
determining whether the images are semantically relevant images via relevance feedback;
estimating a respective semantic correlation between each 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 each image of the pair is indicated to be a semantically relevant image;
wherein the respective semantic correlation is performed offline or online to calculate unigram and bigram frequencies from relevance feedback information, the unigram frequency being based on relevance feedback to a session of the multiple search sessions, the unigram frequency indicating that each respective image of the images is either semantically relevant to the session, semantically less relevant to the session, or a non-feedback image with respect to the session; and
wherein each respective bigram frequency is based on a pair of unigram frequencies. - View Dependent Claims (28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39)
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40. A computing device image retrieval using a statistical bigram correlation model, the computing device comprising:
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processing means for;
receiving a plurality of images responsive to multiple search sessions;
determining whether the images are semantically relevant images via relevance feedback;
estimating a respective semantic correlation between each 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 each image of the pair is indicated to be a semantically relevant image;
wherein the respective semantic correlation is performed offline or online to calculate unigram and bigram frequencies from relevance feedback information, the unigram frequency being based on relevance feedback to a session of the multiple search sessions, the unigram frequency indicating that each respective image of the images is either semantically relevant to the session, semantically less relevant to the session, or a non-feedback image with respect to the session; and
wherein each respective bigram frequency is based on a pair of unigram frequencies. - View Dependent Claims (41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52)
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Specification