Image search result summarization with informative priors
First Claim
1. A method implemented on a computing device having a processor for summarizing image search results, comprising:
- using the computing device having the processor to perform the following;
estimating an image relevance for each image in the image search results as a ranking of each image given by a search engine that provided the image search results in response to a query from a user to the search engine;
computing an image quality for each image based on one or more image quality measures;
clustering images in the image search results using a clustering technique that has as a first informative prior the image quality for each image and as a second informative prior the image relevance for each image to obtain a summary candidate collection containing image clusters and an exemplar image for each cluster;
selecting and ranking each image in the summary candidate collection to obtain an image search results summarization;
selecting a number representing a desired number of summaries; and
presenting the image search results summarization to a user based on whether a number of images contained in the image search results summarization is less than the number representing a desired number of summaries.
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Abstract
An informative priors image search result summarization system and method that summarizes image search results based on the image relevance (as determined by a search engine'"'"'s initial ranking) and the image quality. Embodiments of the system and method cluster the image search results, rank images within each cluster based on a computed image score, and then select a summary image for the cluster. Each cluster is analyzed and an image in the cluster having the maximum image score is included in a selected summary collection. The image score is computed using the image relevance and the image quality, as well as a cluster coherence, a density, and a diversity. The selection of images from a collection of candidate images generates an image search result summarization, which is presented to a user. The summaries are presented to the user in a ranked order based on their image scores.
16 Citations
19 Claims
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1. A method implemented on a computing device having a processor for summarizing image search results, comprising:
using the computing device having the processor to perform the following; estimating an image relevance for each image in the image search results as a ranking of each image given by a search engine that provided the image search results in response to a query from a user to the search engine; computing an image quality for each image based on one or more image quality measures; clustering images in the image search results using a clustering technique that has as a first informative prior the image quality for each image and as a second informative prior the image relevance for each image to obtain a summary candidate collection containing image clusters and an exemplar image for each cluster; selecting and ranking each image in the summary candidate collection to obtain an image search results summarization; selecting a number representing a desired number of summaries; and presenting the image search results summarization to a user based on whether a number of images contained in the image search results summarization is less than the number representing a desired number of summaries. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method implemented on a computing device having a processor for performing image search results summarization on a plurality of initially-ranked search results ranked by a search engine, comprising:
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using the computing device having the processor to perform the following; setting as a first preference an image relevance for each image corresponding to an initial ranking by the search engine; selecting one or more image quality measures; computing an image quality for each image using the selected image quality measures; clustering images in the plurality of initially-ranked search results using a clustering algorithm using the first preference and a second preference to obtain a plurality of clusters; selecting an exemplar image for each of the plurality of clusters and including the exemplar image and images in the plurality of clusters in a summary candidate collection; selecting a cluster and an image from the summary candidate collection; selecting a number representing a desired number of summaries; determining whether a number of images contained in a selected summaries collection is less than the number representing a desired number of summaries; if so, then setting the summary candidate collection equal to images in the plurality of initially-ranked search results minus the number of images contained in the selected summaries collection; computing an image score for each image in the selected cluster using the image relevance, the image quality, a cluster coherence, a density, and a diversity; identifying an image in the selected cluster having a maximum image score as compared to other images in the selected cluster; adding the image having a maximum image score to a selected summaries collection and removing the image having a maximum image score from the summary candidate collection; and displaying to a user images in the selected summary collection in a ranked order based on the image score of the image. - View Dependent Claims (12, 13, 14)
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15. A computer-implemented method for generating summary images for an image search result containing a plurality of initially-ranked images, comprising:
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setting as a first preference an image relevance of the plurality of initially-ranked images, denoted as R(i), which is an image relevance of an image in the ith position of the image search result; computing an image quality of each of the plurality of initially-ranked images using a quality measure based on color entropy, denoted as Q(i), which is an image quality of an image in the ith position of the image search result; clustering the plurality of initially-ranked images using an Affinity Propagation clustering technique having the image relevance as the first preference and the image quality as the second preference to obtain a plurality of clusters; selecting an exemplar image from each of the plurality of clusters and including the exemplars and images in the plurality of clusters in a summary candidate collection; selecting a cluster from the summary candidate collection and an image from the selected cluster; obtaining a cluster coherence, Coh(i), a density, Dens(i), a diversity, Div(i), the image quality, Q(i), and the image relevance, R(i), for an ith image in the selected cluster; computing an image score, Si, for the ith image using the following equation;
Si=W1×
Coh(i)+W2×
Dens(i)+W3×
Div(i)+α
×
R(i)+β
×
Q(i),where W1 is a first weight, W2 is a second weight, W3 is a third weight, α
is a first parameter, and β
is a second parameter, until each image in the selected cluster has an image score;identifying an image in the selected cluster having a maximum image score, removing the identified image from the summary candidate collection, and adding the identified image to a selected summaries collection; and displaying to a user images in the selected summaries collection that are ranked accordingly to a respective image score. - View Dependent Claims (16, 17, 18, 19)
where S(Ii,Ij) is a similarity based on a distance between the ith image, Ii, and a jth image, Ij, and the distance, Dis(i,j), given by the equation;
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18. The computer-implemented method of claim 17, further comprising computing the density, Dens(i), for the ith image in the selected cluster, using the equation:
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Dens(i)=Σ
Ij S(Ii,Ij),where Ij is an image in the image search result other than Ii.
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19. The computer-implemented method of claim 18, further comprising computing the diversity, Div(i), for the ith image in the selected cluster, using the equation:
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Div(i)=maxIj ε
AS(Ii,Ij),where A is a set of selected images.
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Specification