Computationally efficient local image descriptors
AssigneeMicrosoft Corporation
InventorsSimon A. J. Winder, Gang Hua
Life of this patent
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Abstract
Described is a technology in which an image (or image patch) is processed into a highly discriminative and computationally efficient image descriptor that has a low storage footprint. Feature vectors are generated from an image (or image patch), and further processed via a polar Gaussian pooling approach (a DAISY configuration) into a descriptor. The descriptor is normalized, and processed with a dimension reduction component and a quantization component (based upon dynamic range reduction) into a finalized descriptor, which may be further compressed. The resulting descriptors have significantly reduced error rates and significantly smaller sizes than other image descriptors (such as SIFT-based descriptors).
Claims20
In a computing environment, a method comprising, transforming an image into feature vectors based upon features within the image, combining the feature vectors into a descriptor, normalizing the descriptor into a normalized descriptor, and performing dimension reduction on the normalized descriptor to generate a local image descriptor.
In a computing environment, a system comprising, a feature detector that transforms pixels into feature vectors, a summation component that spatially accumulates the feature vectors into a descriptor having a number of dimensions, a dimension reduction component that reduces the number of dimensions of the descriptor, and a quantization component that reduces the reduced-dimensions descriptor into a local image descriptor.
One or more computer-readable media having computer-executable instructions, which when executed perform steps, comprising generating a local image descriptor from an image, including producing a feature vector for each of a set of sample points of the image, spatially accumulating weighted versions of the feature vectors that are combined to form an image descriptor by summing the feature vectors associated with sample points found within a local pooling region relative to a pooling point which is part of a pattern of pooling points located in the image, normalizing the descriptor, and reducing a number of dimensions of the descriptor into the local image descriptor.
Dependencies are inferred from the claim text.