Image Retrieval Network Using Shared Embedding Space for Color-Object Matching
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Solution Overview
Problem
Conventional image retrieval systems are inefficient in handling scenarios with multiple object-color pairs and ambiguous color phrases, such as 'salmon pink', as they fail to accurately associate colors with objects and struggle with complex user queries.
Innovation Solution
An image retrieval network is developed using machine learning to embed object labels and color phrases in a shared embedding space, employing a multi-modal color encoder and query dependency parser to identify and match color phrases with object embeddings, enabling efficient search through databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image retrieval systems use traditional embedding methods, then the system structure remains simple, but the system cannot accurately handle multiple object-color pairs and ambiguous color phrases
Solution Approach 1:
The patent segments the embedding system into separate components: a text encoder for color phrases, an image encoder for object-color pairs, and a shared embedding space. This segmentation allows each component to specialize in handling specific aspects (textual color descriptions vs. visual object-color associations), improving accuracy without overwhelming system complexity
Solution Approach 2:
The patent introduces a shared embedding space as an intermediary that bridges text-based color phrases and image-based object-color pairs. This intermediary representation enables comparison and matching between different modalities (text and image) in a unified semantic space, resolving the contradiction between handling complex queries and maintaining system simplicity
2Measurement precision
If the system addresses each color-object exception individually, then the retrieval accuracy for specific cases improves, but the computational cost and processing time increase significantly
Solution Approach 1:
The patent creates a universal embedding framework that handles diverse color-object scenarios through a single system. The shared embedding space universally represents both text color phrases and image object-color pairs, eliminating the need for separate handling of each exception while maintaining high retrieval accuracy across all cases
3Adaptability or versatility
If the system uses high-dimensional embeddings to differentiate large numbers of objects, then the differentiation capability improves, but the search and comparison complexity increases
Solution Approach 1:
The patent applies local quality by creating specialized encoding pathways: the text encoder focuses on capturing semantic meaning of color phrases, while the image encoder focuses on extracting object-color associations. Each encoder is optimized for its specific input type, enabling high differentiation capability while managing complexity through specialized local processing
Data Source
AI summary
Systems and methods for image retrieval are described. Embodiments of the present disclosure receive a search query from a user; extract an entity and a color phrase describing the entity from the search query; generate an entity color embedding in a color embedding space from the color phrase using a multi-modal color encoder; identify an image in a database based on metadata for the image including an object label corresponding to the extracted entity and an object color embedding in the color embedding space corresponding to the object label; and provide image information for the image to the user based on the metadata.