Image Fingerprinting Model Embedding Conversion
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Solution Overview
Problem
Existing image matching systems are inadequate for identifying matches in images that have undergone transformations such as rotation, cropping, or color distortion, and they require large datasets and extensive processing for classifier training.
Innovation Solution
The use of a deep learning image fingerprinting model to generate semantically rich feature vectors for images, allowing for robust matching even with distortions, and enabling efficient classifier training with minimal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional file hashing methods are used for image matching, then exact image matching is fast and reliable, but the system cannot match images that have been modified, rotated, or cropped
Solution Approach 1:
The patent transforms image data from pixel space to feature space using deep learning models, changing the representation parameters from raw pixels to semantic features. This allows the system to maintain reliability for exact matches while gaining adaptability to distortions, as the feature representations capture semantic content that is invariant to rotation, cropping, and color changes
Solution Approach 2:
The patent replaces traditional mechanical hashing algorithms with deep learning-based embedding models. Instead of using deterministic hash functions that are sensitive to any pixel change, the system uses neural network embeddings that capture semantic meaning, enabling reliable matching of distorted images while maintaining fast comparison through vector similarity search
2Reliability
If deep learning models are retrained to improve performance, then model accuracy improves, but the output embeddings become incompatible with existing indexes
Solution Approach 1:
The patent introduces an embedding conversion model as an intermediary between the original embedding model and the updated model. This converter takes embeddings from either model and transforms them into a unified representation space, maintaining compatibility with the existing index while allowing the system to benefit from improved model accuracy. The converter acts as a bridge that preserves the semantic relationships in the index structure
Solution Approach 2:
The patent pre-trains an embedding conversion model on a large dataset to learn the mapping between different embedding spaces before deployment. This preliminary action ensures that when the model is updated, the conversion mechanism is already optimized and ready to maintain index compatibility, avoiding the need for retraining or reindexing when accuracy improvements are made
3Reliability
If image classifiers are trained with large datasets, then classification accuracy improves, but training time and processing requirements increase significantly
Solution Approach 1:
The patent uses partial action by training classifiers on small, carefully selected subsets of the embedding index rather than requiring complete large-scale datasets. The system leverages the pre-computed semantic embeddings and their organizational structure to achieve effective classification with minimal training samples, dramatically reducing training time while maintaining practical accuracy for most applications
Data Source
AI summary
Image fingerprints (embeddings) are generated by an image fingerprinting model and indexed with an approximate nearest neighbors (ANN) model trained to identify the most similar fingerprint based on a subject embedding. For image matching, a score is provided that indicates a similarity between the input embedding and the most similar identified embedding, which allows for matching even when an image has been distorted, rotated, cropped, or otherwise modified. For image classification, the embeddings in the index are clustered and the clusters are labeled. Users can provide just a few images to add to the index as a labeled cluster. The ANN model returns a score and label of the most similar identified embedding for labeling the subject image if the score exceeds a threshold. As improvements are made to the image fingerprinting model, a converter model is trained to convert the original embeddings to be compatible with the new embeddings.