Image Embedding Vector Metrics for Semantic Discrepancy Detection
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Solution Overview
Problem
Conventional image embedding methods fail to correlate parameters associated with images, such as edges and shapes, leading to loss of semantic meaning and inefficiency in identifying discrepancies, especially on resource transfer between entities using portable devices with limited processing power.
Innovation Solution
Utilizing image embeddings generated by a trained generative model to convert images into numerical representations, allowing for vector distance metrics to measure similarity and detect discrepancies, which are robust against orientation and transformation variations, enabling precise and quick identification of anomalies on edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image embedding methods are used to compress images into lower-dimensional vectors, then processing speed and resource efficiency are improved, but semantic meaning of image parameters such as edges and shapes is lost
Solution Approach 1:
The patent transforms image parameters by applying geometric transformations (rotation, scaling, translation) to create multiple views of the same image features. This allows the embedding system to learn parameter-invariant representations that maintain semantic meaning while enabling efficient comparison through vector distance metrics
Solution Approach 2:
The patent extends traditional 2D image space into a higher-dimensional parameter space by incorporating transformation variables. This allows the system to preserve semantic relationships in the embedding while reducing computational complexity through vector space operations
2Use of energy by moving object
If conventional image embedding methods are used, then resource consumption is reduced, but the ability to identify discrepancies accurately deteriorates
Solution Approach 1:
The patent introduces vector distance metrics as an intermediary between image embeddings and discrepancy detection. This mediator enables efficient resource-consuming operations to be performed in the compressed vector space while maintaining high accuracy in identifying image discrepancies
Solution Approach 2:
The patent replaces complex mechanical image comparison operations with vector space mathematical operations. This substitution maintains measurement precision while significantly reducing computational resource requirements
3Productivity
If complex image parameters such as edges and shapes are compressed into lower-dimensional vectors, then processing efficiency is improved, but the correlation between associated parameters is lost
Solution Approach 1:
The patent performs preliminary geometric transformations on image parameters before embedding, pre-establishing the correlation relationships. This allows the subsequent vector compression to maintain these correlations while achieving processing efficiency
Data Source
AI summary
Techniques are described herein for correlating vector metrics for image embedding processing. Techniques can include receiving an image associated with an article, where the image represents a user-defined feature of the article and providing, to a generative model configured to generate image embeddings. An image embedding may be received from the generative model associated with the image associated with the article representing the transfer of the at least one resource from the first entity to the second entity, the image embedding encoding at least the user-defined feature of the article. A vector distance metric may be determined based at least in part on a vector distance comparison between the image embedding and a prior image embedding and one or more operations of a computer system may be controlled based at least in part on the vector distance metric.