Interactive Scene Graph Search With GNN Node Alignment
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Solution Overview
Problem
Existing graph matching algorithms are inefficient for interactive queries from humans and struggle with accurate one-to-one node correspondence, especially in large databases, making it difficult to visualize and interpret subgraph patterns effectively.
Innovation Solution
A system utilizing Graph Neural Networks (GNNs) for encoding topological and node attributes in graphs as fixed-length vectors, employing NeuroMatch for subgraph matching and NeuroAlign for node alignment, enabling efficient and accurate one-to-one node correspondence visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional graph matching algorithms are used for subgraph pattern search, then completeness of matching can be achieved, but computational efficiency deteriorates significantly in large databases
Solution Approach 1:
The patent replaces traditional mechanical graph matching algorithms with a neural network-based system. The GNN encoder learns graph representations and the similarity calculator computes matching scores through neural network operations, substituting the step-by-step mechanical matching process with parallel neural computation that achieves both accuracy and efficiency.
Solution Approach 2:
The patent transforms the graph matching problem from discrete structural comparison to continuous vector space comparison. By encoding graphs as continuous vectors through GNN and computing similarity in this transformed parameter space, the system achieves efficient computation while maintaining matching accuracy.
2Measurement precision
If detailed node-level analysis is performed for subgraph matching, then node correspondence accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary graph encoding into fixed-length vectors using GNN before the actual matching process. This pre-computation of graph representations allows the system to work with compact vectors rather than full graph structures during matching, reducing computational complexity while preserving node-level correspondence information.
Solution Approach 2:
The patent introduces graph embedding vectors as an intermediary representation between the original graph structure and the matching process. These vectors serve as a compressed intermediary that captures essential structural and attribute information, enabling efficient comparison without directly processing the full graph complexity.
3Ease of operation
If interactive query interface with visual feedback is provided, then user ease of operation improves, but system complexity increases
Solution Approach 1:
The patent creates visual copies and representations of the query graph and matching results through graphical user interface elements. The system generates visual feedback that replicates the graph structure and highlighting information, allowing users to interact with intuitive visual representations rather than complex data structures directly.
Data Source
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AI summary
Methods and systems for providing an interactive image scene graph pattern search are provided. A user is provide with an image having a plurality of selectable segmented regions therein. The user selects one or more of the segmented regions to build a query graph. Via a graph neural network, matching target graphs are retrieved that contain the query graph from a target graph database. Each matching target graph has matching target nodes that match with the query nodes of the query graph. Matching target images from an image database are associated with the matching target graphs. Embeddings of each of the query nodes and the matching target nodes are extracted. A comparison of the embeddings of each query node with the embeddings of each matching target node is performed. The user interface displays the matching target images that are associated with the matching target graphs.