Image Feature Descriptor Subspace Segmentation
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Solution Overview
Problem
Current image search and retrieval systems face challenges in efficiently handling high-dimensional feature descriptors, leading to slow performance due to the complexity of clustering and quantizing large numbers of features, especially in large databases.
Innovation Solution
The method involves transforming feature descriptors into sub-descriptors using various transformation schemes, allowing for lower-dimensional subspaces that can be quantized more efficiently, enabling the determination of feature primitives for image characterization and representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-dimensional feature descriptors are used for comprehensive image characterization, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides high-dimensional feature descriptors into multiple lower-dimensional sub-descriptors that correspond to different subspaces of the feature descriptor space. Each subspace is quantized separately using cluster analysis, which reduces the overall computational complexity while maintaining the comprehensive characterization capability of the original high-dimensional descriptors.
2Manufacturing precision
If clustering and quantization are applied to large numbers of high-dimensional features, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The patent segments the large set of high-dimensional features into multiple smaller subsets corresponding to different subspaces. Each subspace is quantized independently through cluster analysis, which significantly reduces the computational burden compared to quantizing all features together, thereby improving processing speed while maintaining quantization accuracy.
Solution Approach 2:
The patent transforms the problem from quantizing high-dimensional features directly to quantizing lower-dimensional subspaces. By changing the dimensionality of the feature representation, the patent reduces computational complexity and improves processing throughput while preserving the essential information needed for accurate image characterization.
3Measurement precision
If high-dimensional feature descriptors are used for detailed image representation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent divides the time-consuming quantization process into multiple parallel operations on lower-dimensional subspaces. By segmenting the feature descriptor space into subspaces and quantizing each subspace separately, the patent reduces the overall processing time while maintaining the precision of image feature characterization.
Data Source
AI summary
A method, system, and computer-readable storage medium for characterizing and representing images. A plurality of feature descriptors for a plurality of images are received, where each feature descriptor encodes a respective feature in an image, and where each feature descriptor is transformed into a plurality of sub-descriptors in accordance with a specified transformation scheme. The feature descriptors correspond to an image feature descriptor space for the plurality of images, and the sub-descriptors correspond to a plurality of subspaces of the image feature descriptor space, where the plurality of subspaces span the image feature descriptor space. Each subspace of the plurality of subspaces is quantized using cluster analysis applied to the sub-descriptors, thereby determining a respective one or more feature primitives for each subspace, where the feature primitives are useable to characterize image features.