Multi-Modal Image Similarity via Learned Embedding Space
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Solution Overview
Problem
Existing computer vision methods face challenges in determining similarity between visual data from different modalities, such as images captured using various spectral bands or medical imaging techniques, due to data heterogeneity and complexity, especially in large-scale Internet data analysis, where standard distance metrics fail to capture true similarity and are computationally intensive.
Innovation Solution
A method involving embedding functions that map images from different modalities into a representation space where the distance between representations indicates similarity, using a training set with labeled pairs and triplets to optimize the embedding functions, and employing a Hamming distance in a binary vector space to determine similarity, allowing for efficient comparison of images across different modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard distance metrics (e.g., Euclidean metric) are used to compare feature descriptors, then the computation is simple and fast, but the true similarity between descriptors cannot be captured due to non-Euclidean structure of descriptor space
Solution Approach 1:
The patent transforms the descriptor space from a fixed Euclidean metric to a learned metric space by introducing a distance metric learning component. The system learns an optimal distance metric through training on labeled data, where the metric parameters are adjusted to best capture similarity relationships. This allows the system to maintain computational efficiency while achieving accurate similarity measurement by adapting the metric to the specific characteristics of the descriptor space.
Solution Approach 2:
The patent implements a feedback mechanism through iterative optimization where the distance metric is refined based on training data. The system uses labeled pairs of descriptors with known similarity relationships to continuously adjust and optimize the distance metric parameters. This feedback loop enables the system to converge on an optimal metric that accurately reflects true similarity while maintaining efficient computation.
2Reliability
If feature-based approaches with local feature detection and description are used, then correspondence finding becomes possible, but the computational complexity increases significantly for large-scale image sets
Solution Approach 1:
The patent extracts and utilizes only the most discriminative and informative features from the full image data. By focusing on learned distance metrics that highlight the most significant similarity relationships, the system can work with a reduced set of critical features rather than processing all possible local features. This extraction approach maintains high correspondence accuracy while significantly reducing computational complexity for large-scale applications.
Solution Approach 2:
The patent segments the image comparison task into multiple stages: first extracting local features, then computing their representations, and finally comparing them using the learned distance metric. This segmentation allows the system to handle large-scale image sets by processing features in manageable chunks rather than attempting to compare entire images at once, thereby reducing overall computational complexity.
3Ease of manufacture
If affine transformation model is used to approximate image transformations, then the model is simple and computationally efficient, but it fails to account for complicated real-world transformations including illumination changes, lens distortions, and perspective effects
Solution Approach 1:
The patent changes the parameters of the transformation model by learning optimal metric parameters from training data that captures complex real-world transformations. Instead of using a fixed simple affine model, the system adapts the distance metric parameters to account for illumination changes, lens distortions, and perspective effects. This parameter adaptation maintains model simplicity in terms of computation while significantly improving transformation invariance and reliability.
Solution Approach 2:
The patent introduces dynamics into the transformation model by making the distance metric adaptive rather than static. The learned metric dynamically adjusts to the specific characteristics of the data and transformations present in the application domain. This dynamic adaptation allows the system to handle complex real-world transformations effectively while maintaining computational efficiency through the optimized metric parameters.
Data Source
AI summary
A method determines similarity of objects depicted in images when the images pertain to different modalities. The method includes obtaining images that depict the objects and that pertain to the different modalities. An embedding function is applied to each of the images. The embedding function is selected from a set of two or more embedding functions, each of the embedding functions corresponding to a modality of the different modalities, the selected embedding function corresponding to the modality of the image to which it is applied. Application of the embedding function maps that image to a representation in a representation space such that when the images are mapped to the representation space, a distance between the representations of the images is indicative of a similarity of their depicted objects. The similarity of the depicted objects is determined based on the location of the corresponding representations in the representation space.