FAST COMPUTATION OF KERNEL DESCRIPTORS
First Claim
1. A method for processing images comprising:
- reading a plurality of stored kernel tables, each kernel table representing a mapping from a corresponding feature to a vector of values;
accepting images for processing, and identifying a plurality of patches within said images;
computing a feature vector F(P) for each patch P of the plurality of patches, including computing one or more summations over locations z in the patch P of terms, each term being a product of terms including a term obtained by a lookup in a corresponding kernel table according to the location z and/or an attribute of the patch P at the location z; and
processing the images according to the computed feature vectors for the plurality of patches.
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Abstract
An approach to computation of kernel descriptors is accelerated using precomputed tables. In one aspect, a fast algorithm for kernel descriptor computation that takes O(1) operations per pixel in each patch, based on pre-computed kernel values. This speeds up the kernel descriptor features under consideration, to levels that are comparable with D-SIFT and color SIFT, and two orders of magnitude faster than STIP and HoG3D. In some examples, kernel descriptors are applied to extract gradient, flow and texture based features for video analysis. In tests of the approach on a large database of internet videos used in the TRECVID MED 2011 evaluations, the flow based kernel descriptors are up to two orders of magnitude faster than STIP and HoG3D, and also produce significant performance improvements. Further, using features from multiple color planes produces small but consistent gains.
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Citations
9 Claims
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1. A method for processing images comprising:
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reading a plurality of stored kernel tables, each kernel table representing a mapping from a corresponding feature to a vector of values; accepting images for processing, and identifying a plurality of patches within said images; computing a feature vector F(P) for each patch P of the plurality of patches, including computing one or more summations over locations z in the patch P of terms, each term being a product of terms including a term obtained by a lookup in a corresponding kernel table according to the location z and/or an attribute of the patch P at the location z; and processing the images according to the computed feature vectors for the plurality of patches. - View Dependent Claims (2, 3, 4, 5)
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6. A method for image processing comprising:
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reading a plurality of stored kernel tables, each kernel table representing a mapping from a corresponding feature to a vector of values; accepting images for processing, and identifying patches within said images; repeatedly computing similarities between pairs of patches for images being processed, computation of a similarity between a patch P and a patch Q comprises computing for patch P one or more summations over locations z in the patch P of terms, each term being a product of terms including a term obtained by a lookup in a corresponding kernel table according to the location z and/or an attribute of the patch P at the location z, computing for patch Q one or more summations over locations z in the patch Q of terms, each term being a product of terms including a term obtained by a lookup in a corresponding kernel table according to the location z and/or an attribute of the patch Q at the location z, and combining the sums of the one or more summations for P and one or more summations for Q to determine a kernel descriptor similarity between P and Q; and providing a result of processing the images determined using the computed similarities between the patches. - View Dependent Claims (7)
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8. Software stored on a non-transitory computer-readable medium comprising instructions for causing a processor to:
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read a plurality of stored kernel tables, each kernel table representing a mapping from a corresponding feature to a vector of values; accept images for processing, and identifying a plurality of patches within said images; compute a feature vector F (P) for each patch P of the plurality of patches, including computing one or more summations over locations z in the patch P of terms, each term being a product of terms including a term obtained by a lookup in a corresponding kernel table according to the location z and/or an attribute of the patch P at the location z; and process the images according to the computed feature vectors for the plurality of patches.
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9. Software stored on a non-transitory computer-readable medium comprising instructions for causing a processor to:
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read a plurality of stored kernel tables, each kernel table representing a mapping from a corresponding feature to a vector of values; accept images for processing, and identifying patches within said images; repeatedly compute similarities between pairs of patches for images being processed, computation of a similarity between a patch P and a patch Q comprises computing for patch P one or more summations over locations z in the patch P of terms, each term being a product of terms including a term obtained by a lookup in a corresponding kernel table according to the location z and/or an attribute of the patch P at the location z, computing for patch Q one or more summations over locations z in the patch Q of terms, each term being a product of terms including a term obtained by a lookup in a corresponding kernel table according to the location z and/or an attribute of the patch Q at the location z, and combining the sums of the one or more summations for P and one or more summations for Q to determine a kernel descriptor similarity between P and Q; and provide a result of processing the images determined using the computed similarities between the patches.
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Specification