AI Image Search Refinement for Complex Query Relationships
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Solution Overview
Problem
Traditional search techniques struggle with complex queries, including those with multiple objects and spatial relationships, often producing incomplete or irrelevant image search results due to insufficient token windows and difficulty in understanding query term relationships.
Innovation Solution
A computing system utilizing a generative model to process search queries, determine candidate image search results, and adjust rankings based on scores generated by processing the queries with an embedding model and auto-rater to refine search results, incorporating external classification models for improved relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search techniques are used to process complex queries with multiple objects and spatial relationships, then the search system maintains simplicity in architecture, but the search result accuracy and completeness deteriorate
Solution Approach 1:
The patent segments the complex search query processing into multiple independent components: an embedding model that converts queries and images into vector representations, a retrieval system that identifies candidate images based on embedding similarity, and a generative model that verifies and refines results against the original query. This segmentation allows each component to specialize in one aspect of the task, improving overall accuracy without requiring a monolithic complex system.
Solution Approach 2:
The patent introduces embedding vectors as an intermediary representation between the query and the image database. Instead of directly comparing complex query structures with images, the system converts both into embedding spaces where semantic similarity can be efficiently measured. This intermediary layer simplifies the matching process while maintaining high accuracy in retrieving relevant images.
2Loss of information
If traditional search systems use limited token windows for query processing, then the system maintains fast processing speed, but the ability to understand relationships between query terms deteriorates
Solution Approach 1:
The patent replaces traditional mechanical keyword-matching search mechanisms with machine learning-based embedding models. Instead of relying on exact keyword matches or limited token window analysis, the system uses neural network embeddings that capture semantic relationships between terms regardless of their position or exact wording. This substitution enables comprehensive understanding of query relationships without being constrained by token window limitations.
3Reliability
If traditional search techniques are used for complex queries with actions and settings, then the system maintains simplicity in query processing, but the search result relevance deteriorates
Solution Approach 1:
The patent performs preliminary processing of the query by the embedding model to create a comprehensive vector representation that encodes all query elements (objects, actions, settings) and their relationships before retrieval begins. This preliminary action ensures that complex query requirements are fully captured in the embedding space, allowing the retrieval and verification stages to focus on finding and confirming matches rather than parsing complex query structures.
Data Source
AI summary
Systems and methods for image search result filtering can include obtaining a search query, determining a plurality of candidate image search results, processing the search query with a generative model to determine a plurality of search result criteria, and refining the plurality of candidate image search results based on determining whether the candidate results satisfy the plurality of search results criteria. The systems and methods can perform a plurality of determinations based on the output of the generative model.