Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR)
First Claim
1. A method for improving iterative results of Content-Based Image Retrieval (CBIR) using relevance feedback, the method comprising:
- obtaining positive feedback images and negative feedback images via relevance feedback, the positive feedback images are those images deemed semantically relevant and the negative feedback images are those deemed semantically less relevant;
within a feature space, moving positive candidate images towards the positive feedback images, the positive candidate images have similar low-level features as those of positive feedback images;
within a feature space, distancing negative candidate images from the positive feedback images, the negative candidate images have similar low-level features as those of negative feedback images.
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Abstract
An implementation of a technology, described herein, for relevance-feedback, content-based facilitating accurate and efficient image retrieval minimizes the number of iterations for user feedback regarding the semantic relevance of exemplary images while maximizing the resulting relevance of each iteration. One technique for accomplishing this is to use a Bayesian classifier to treat positive and negative feedback examples with different strategies. In addition, query refinement techniques are applied to pinpoint the users'"'"' intended queries with respect to their feedbacks. These techniques further enhance the accuracy and usability of relevance feedback. This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.
38 Citations
29 Claims
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1. A method for improving iterative results of Content-Based Image Retrieval (CBIR) using relevance feedback, the method comprising:
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obtaining positive feedback images and negative feedback images via relevance feedback, the positive feedback images are those images deemed semantically relevant and the negative feedback images are those deemed semantically less relevant;
within a feature space, moving positive candidate images towards the positive feedback images, the positive candidate images have similar low-level features as those of positive feedback images;
within a feature space, distancing negative candidate images from the positive feedback images, the negative candidate images have similar low-level features as those of negative feedback images. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for improving iterative results of Content-Based Image Retrieval (CBIR) using relevance feedback, the method comprising:
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obtaining positive feedback images and negative feedback images via relevance feedback, the positive feedback images are those images deemed semantically relevant and the negative feedback images are those deemed semantically less relevant;
with the positive feedback images, updating parameters of a Bayesian classifier;
according to the negative feedback images, applying a dibbling process. - View Dependent Claims (9, 10, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 24, 25, 27, 29)
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13. A method for improving the evaluation of the similarly of images based upon image features, the method comprising calculating a weighted similarity distance between a first image and a set of images as a distance between them in a feature space.
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17. A method for improving the evaluation of the similarly of images based upon image features, the method comprising defining a subject image to be similar to an example image because it is within a minimal distance of the example image within a feature space.
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23. A system for improving iterative results of Content-Based Image Retrieval (CBIR) using relevance feedback, the system comprising:
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a relevance feedback sub-system configured to obtain positive feedback images and negative feedback images via relevance feedback, the positive feedback images are those images deemed semantically relevant and the negative feedback images are those deemed semantically less relevant;
a feedback analyzer configured, within a feature space, to;
move positive candidate images towards the positive feedback images, the positive candidate images have similar low-level features as those of positive feedback images;
distance negative candidate images from the positive feedback images, the negative candidate images have similar low-level features as those of negative feedback images.
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26. A system for improving iterative results of Content-Based Image Retrieval (CBIR) using relevance feedback, the system comprising:
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a memory comprising a set of computer program instructions; and
a processor coupled to the memory, the processor being configured to execute the computer program instructions, which comprise;
obtaining positive feedback images and negative feedback images via relevance feedback, the positive feedback images are those images deemed semantically relevant and the negative feedback images are those deemed semantically less relevant;
within a feature space, moving positive candidate images towards the positive feedback images, the positive candidate images have similar low-level features as those of positive feedback images;
within a feature space, distancing negative candidate images from the positive feedback images, the negative candidate images have similar low-level features as those of negative feedback images.
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28. A computer-readable medium having computer-executable instructions that, when executed by a computer, performs the method comprising:
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obtaining positive feedback images and negative feedback images via relevance feedback, the positive feedback images are those images deemed semantically relevant and the negative feedback images are those deemed semantically less relevant;
with the positive feedback images, updating parameters of a Bayesian classifier;
dibbling the negative feedback images.
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Specification