Continuous Indexing for Image Matching Systems

The patent transforms image features from traditional discrete descriptors (bag-of-visual-words, local feature descriptors) to continuous embedding vectors generated by deep neural networks. This parameter transformation enables the system to capture semantic information more effectively, improving retrieval accuracy while reducing sensitivity to appearance and lighting changes. The continuous nature of the embeddings allows for smoother feature spaces and better generalization.

The patent replaces traditional mechanical indexing mechanisms with a neural network-based embedding system. Instead of using hand-crafted features and discrete visual words, the system uses learned continuous representations from deep neural networks. This substitution eliminates the discontinuity and high dimensionality problems of traditional methods while maintaining scalability.

The patent implements a dynamic indexing system where the feature index is continuously updated with new images rather than being static. The system maintains an active index that can accommodate new images without requiring complete rebuilding. This dynamic approach allows the system to adapt to changing image data while maintaining retrieval efficiency, as the index structure remains stable while individual entries are updated incrementally.

The patent pre-processes and indexes images in advance, creating a searchable feature index that can be queried efficiently. The system performs preliminary feature extraction and indexing operations so that when new images arrive, they can be matched against the existing index without requiring full reindexing. This preliminary action enables fast retrieval while the system continues to update with new data.

The patent extracts the most relevant information from high-dimensional neural network embeddings by projecting them into a lower-dimensional searchable index. Instead of querying the full high-dimensional embedding space directly, the system extracts key features and creates a compressed index structure that maintains the essential semantic information while reducing computational complexity for retrieval operations.

The patent introduces an intermediate indexing layer between the neural network embeddings and the retrieval query. This intermediate structure acts as a mediator that translates high-dimensional continuous data into a format suitable for efficient searching. The intermediary index structure enables fast approximate nearest neighbor search while preserving the semantic meaning of the deep learning features.