Scene Graph Embeddings for Semantic Image Retrieval
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Solution Overview
Problem
Conventional image retrieval systems are limited in capturing semantic information and higher-order concepts, relying mainly on visually discriminative features, which leads to inaccurate results for complex images with multiple objects and relationships.
Innovation Solution
The implementation of a scene graph-based approach using a graph convolutional network (GCN) to generate scene graph embeddings, incorporating contrastive learning techniques and efficient sampling methods, allows for the capture of semantic information and relationships in images, enabling more accurate semantic image retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional image retrieval systems use visually discriminative features, then the system is simple to implement, but the system fails to capture semantic information and higher-order concepts
Solution Approach 1:
The patent segments the image representation into multiple components: visual features, scene graphs (with nodes for objects and edges for relationships), and embeddings. This segmentation allows the system to capture semantic information at different levels of abstraction while managing complexity through modular processing of each component
Solution Approach 2:
The patent introduces scene graphs as an intermediary representation between raw visual features and final retrieval results. Scene graphs serve as a mediator that structures semantic information about objects and their relationships, enabling the system to capture higher-order concepts without directly processing complex raw image data
2Measurement precision
If the system captures higher-order concepts and semantic information, then retrieval accuracy improves, but computational complexity increases
Solution Approach 1:
The patent transforms the scene graph structure into compact embedding vectors that capture semantic information in a condensed form. This parameter transformation from detailed scene graphs to compressed embeddings maintains retrieval accuracy while reducing computational complexity for comparison and search operations
Solution Approach 2:
The patent transitions from structured scene graph data to continuous embedding space representations. This dimensional transformation allows semantic information to be captured in a different representational space that is more efficient for computational operations while preserving the semantic relationships needed for accurate retrieval
3Reliability
If the system uses scene graph embeddings with contrastive learning, then semantic gap is bridged, but training complexity increases
Solution Approach 1:
The patent implements contrastive learning with ranking loss that provides feedback during training to optimize the embedding space. The loss function uses pairwise comparisons to guide the learning process, reinforcing semantic relationships while the efficiency improvements reduce the burden of this iterative training process
4Measurement precision
If the system processes complex images with multiple objects and relationships, then retrieval relevance improves, but processing time increases
Solution Approach 1:
The patent extracts key semantic elements from complex images by generating scene graphs that identify important objects and relationships. This extraction process filters out redundant visual information while preserving the essential semantic content needed for relevant retrieval, reducing processing time while maintaining accuracy
Data Source
AI summary
Systems and methods for image processing are described. One or more embodiments of the present disclosure identify an image including a plurality of objects, generate a scene graph of the image including a node representing an object and an edge representing a relationship between two of the objects, generate a node vector for the node, wherein the node vector represents semantic information of the object, generate an edge vector for the edge, wherein the edge vector represents semantic information of the relationship, generate a scene graph embedding based on the node vector and the edge vector using a graph convolutional network (GCN), and assign metadata to the image based on the scene graph embedding.