Universal Mixture Model Adaptation for Image Comparison
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Solution Overview
Problem
Existing image comparison methods using mixture models, such as Gaussian Mixture Models (GMMs), face challenges in robustly determining model parameters due to sparse data sets, leading to inaccurate comparisons and computationally intensive operations, especially when dealing with large databases.
Innovation Solution
Adapting a universal mixture model to objects to generate corresponding mixture models with a priori component correspondence, allowing for component-by-component comparisons that reduce computational complexity and improve robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a Gaussian mixture model with many components is used to adequately describe images, then the descriptive adequacy is improved, but the computational complexity and parameter determination difficulty increase substantially
Solution Approach 1:
The patent applies preliminary action by pre-defining a fixed set of visual word codes (visual vocabulary) before image comparison. This visual vocabulary serves as a universal codebook that is adapted to different images, allowing the system to avoid complex parameter estimation during comparison while maintaining descriptive adequacy through the predefined visual words.
Solution Approach 2:
The patent changes the approach from estimating mixture model parameters (means, covariances, weights) for each image to using a fixed visual vocabulary with adaptive weighting. This parameter change simplifies the model representation while maintaining the ability to describe images adequately through the visual word distributions.
2Reliability
If a Gaussian mixture model with many components is used to characterize images, then the descriptive content is improved, but the robustness of parameter determination deteriorates due to sparse data sets
Solution Approach 1:
The patent performs preliminary action by pre-establishing a comprehensive visual vocabulary from training data before actual image comparison. This visual vocabulary aggregates information from many images, providing robust statistical foundations that compensate for the sparseness of individual image feature vectors. The visual words are adaptively weighted for each image without requiring complex parameter estimation from sparse data.
Solution Approach 2:
The patent merges information across multiple images to build a shared visual vocabulary. By combining feature statistics from training images to define the visual word codes, the system creates a robust reference framework that improves parameter determination accuracy for individual images through the aggregated information.
3Measurement precision
If pairwise comparison of all Gaussian components is performed for image comparison, then the comparison accuracy is improved, but the computational time increases prohibitively for large databases
Solution Approach 1:
The patent applies preliminary action by pre-defining the visual vocabulary and establishing the correspondence between visual words and Gaussian components before image comparison. This allows the system to directly compare visual word distributions using simple arithmetic operations rather than performing complex pairwise Gaussian comparisons during the actual image matching process.
Solution Approach 2:
The patent segments the image representation into discrete visual word codes that map to specific Gaussian components. This segmentation allows for efficient comparison by operating on the discrete visual vocabulary distributions rather than continuous Gaussian parameters, significantly reducing computational complexity while maintaining comparison accuracy.
4Productivity
If a universal mixture model with fixed components is adapted to images, then the computational efficiency is improved, but the adaptability to different image characteristics may be reduced
Solution Approach 1:
The patent performs preliminary action by pre-defining a comprehensive visual vocabulary that covers a wide range of visual concepts. This universal visual vocabulary is then adaptively weighted for each image based on its specific characteristics, maintaining both computational efficiency and adaptability through the combination of fixed structure and flexible weighting.
Solution Approach 2:
The patent changes the approach from adapting the entire mixture model structure to adapting only the visual word weights for each image. This parameter change maintains computational efficiency by keeping the visual vocabulary fixed while achieving adaptability through image-specific weight adjustments that reflect different image characteristics.
Data Source
AI summary
Object comparison is disclosed, including: adapting N universal mixture model components to a first object to generate N corresponding first object mixture model components, where N is an integer greater than or equal to two; and generating a similarity measure based on component-by-component comparison of the N first object mixture model components with corresponding N second object mixture model components obtained by adaptation of the N universal mixture model components to a second object.