GIMS: Image Matching System Based on Adaptive Graph Construction and Graph Neural Network (2412.18221v1)

Abstract: Feature-based image matching has extensive applications in computer vision. Keypoints detected in images can be naturally represented as graph structures, and Graph Neural Networks (GNNs) have been shown to outperform traditional deep learning techniques. Consequently, the paradigm of image matching via GNNs has gained significant prominence in recent academic research. In this paper, we first introduce an innovative adaptive graph construction method that utilizes a filtering mechanism based on distance and dynamic threshold similarity. This method dynamically adjusts the criteria for incorporating new vertices based on the characteristics of existing vertices, allowing for the construction of more precise and robust graph structures while avoiding redundancy. We further combine the vertex processing capabilities of GNNs with the global awareness capabilities of Transformers to enhance the model's representation of spatial and feature information within graph structures. This hybrid model provides a deeper understanding of the interrelationships between vertices and their contributions to the matching process. Additionally, we employ the Sinkhorn algorithm to iteratively solve for optimal matching results. Finally, we validate our system using extensive image datasets and conduct comprehensive comparative experiments. Experimental results demonstrate that our system achieves an average improvement of 3.8x-40.3x in overall matching performance. Additionally, the number of vertices and edges significantly impacts training efficiency and memory usage; therefore, we employ multi-GPU technology to accelerate the training process. Our code is available at https://github.com/songxf1024/GIMS.

• The paper introduces GIMS, an image matching system using adaptive graph construction to build efficient similarity-based graphs for better data pattern preservation.
• GIMS employs a hybrid model combining Graph Neural Networks for local processing and Transformers for global awareness, enhancing accuracy and robustness in diverse scenarios.
• Experimental results show GIMS outperforms existing methods on large datasets, achieving significantly higher pose estimation accuracy and valid match numbers (3.8x to 40.3x) across various image transformations.

Overview of "GIMS: Image Matching System Based on Adaptive Graph Construction and Graph Neural Network"

This paper introduces GIMS, an advanced image matching system leveraging adaptive graph construction and Graph Neural Networks (GNNs). The proposed system addresses key challenges in traditional and deep learning-based image matching methods by integrating graph structures and improving computational efficiency and matching accuracy.

Key Contributions and Methods

1. Adaptive Graph Construction: The system employs a novel similarity-based adaptive graph construction method. This approach minimizes redundancy in vertices and edges by selectively creating connections between highly similar vertex pairs. The adaptive mechanism dynamically adjusts based on the characteristics of existing vertices, optimizing the graph density and preserving intrinsic data patterns.
2. Hybrid Model Integration: GIMS combines the strengths of GNNs and Transformers. GNNs enhance local vertex processing capabilities by aggregating information from neighboring vertices, while Transformers provide global awareness by capturing long-distance dependencies. This hybrid model effectively integrates local structures with global spatial and feature information, enhancing the robustness and accuracy of image matching.
3. Multi-GPU Training Optimization: The system employs multi-GPU technology to accelerate the training process, addressing the computational demands of large datasets typical for GNN and Transformer models. This approach enhances training efficiency and facilitates the handling of complex image datasets.
4. Graph Matching Approach: GIMS uses the Sinkhorn algorithm to solve optimal transport problems for graph matching. By iteratively optimizing the cost matrix, the algorithm achieves efficient and precise vertex matching between image pairs.

Experimental Results

Experimental validation on large-scale datasets such as COCO2017, RGB-D, and Oxford-Affine demonstrates the superiority of GIMS over existing image matching methods. The system shows significant improvements in pose estimation accuracy, with AUC increases observed across various image environments. GIMS achieves an average improvement of 3.8x to 40.3x in valid match numbers, indicating its robustness in diverse scenarios, ranging from perspective and distance changes to rotational transformations.

Implications and Future Directions

The integration of adaptive graph construction and GNNs in GIMS points towards a promising direction for enhancing image matching systems. By addressing the limitations in conventional methods and effectively combining local and global information processing capabilities, GIMS sets a benchmark for future research in the domain.

Future work could explore more efficient graph construction techniques, advanced GNN architectures, and optimal transport algorithms to further enhance the performance of image matching systems. Moreover, the exploration of parallel and distributed training could offer new insights into improving computational efficiency for GNN-based frameworks handling large datasets.

In conclusion, this paper presents a comprehensive approach to improving image matching with state-of-the-art techniques in graph theory and machine learning, potentially informing subsequent advancements in computer vision applications.