Camera-Specific Generative Models for Image Signature Matching
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Solution Overview
Problem
Existing image matching technologies face challenges in accurately matching objects across images captured under different imaging conditions, such as varying angles, backgrounds, lighting, and weather, which affects feature distribution and classifier performance in object recognition tasks.
Innovation Solution
A system and method that adapts a universal generative model to create camera-specific models, using Fisher Vectors to encode deviations specific to image content rather than imaging conditions, enabling effective object re-identification across different camera perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a universal generative model is used for all cameras, then the system complexity is reduced and training is simplified, but the matching accuracy deteriorates due to imaging condition variations
Solution Approach 1:
The patent segments the universal generative model into camera-specific generative models by adapting the model parameters for each individual camera. This allows each camera to have its own tailored model while maintaining a unified framework, thereby resolving the contradiction between system simplicity and matching accuracy.
Solution Approach 2:
The patent applies local quality by customizing the generative model parameters specifically for each camera's imaging characteristics. Instead of using a one-size-fits-all approach, the model is adapted to capture the unique features and conditions of each camera, improving matching accuracy without significantly increasing overall system complexity.
2Measurement precision
If camera-specific generative models are trained independently, then the matching accuracy improves by accounting for imaging conditions, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-adapting the universal generative model parameters for each camera using a dataset captured by that camera before actual matching operations. This preparation step ensures that when matching occurs, the models are already optimized for each camera's specific conditions, reducing the need for repeated training and lowering computational overhead during operational phases.
3Loss of information
If Fisher Vectors encode all feature deviations, then comprehensive image representation is achieved, but the impact of imaging condition variations cannot be distinguished from object differences
Solution Approach 1:
The patent extracts and separates the imaging condition variations from the object features by using camera-specific generative models. These models learn to encode the characteristics of each camera's imaging conditions, allowing the Fisher Vectors to focus on object-specific deviations while filtering out camera-induced variations. This extraction process resolves the contradiction by removing the harmful component (imaging condition noise) while preserving the useful information (object features).
Data Source
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AI summary
A method includes adapting the universal generative model of local descriptors to a first camera to obtain a first camera-dependent generative model. The same universal generative model is also adapted to a second camera to obtain a second camera-dependent generative model. From a first image captured by the first camera, a first image-level descriptor is extracted, using the first camera-dependent generative model. From a second image captured by the second camera, a second image-level descriptor is extracted using the second camera-dependent generative model. A similarity is computed between the first image-level descriptor and the second image-level descriptor. Information is output, based on the computed similarity. The adaptation allows differences between the image-level descriptors to be shifted towards deviations in image content, rather than the imaging conditions.