Graph Neural Network for Fine-Grained Image Recognition
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Solution Overview
Problem
Existing fine-grained image recognition methods rely on additional component feature extraction networks, which lead to unstable feature representations and affect the recognition effect.
Innovation Solution
A fine-grained recognition method using graph structure represented high-order relation discovery, which extracts graph features through a single-stage network without additional component feature extraction networks, by constructing a hybrid high-order attention module and utilizing semantic similarity to form representative vector nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional component feature extraction networks are used to enhance recognition, then recognition precision may improve, but device complexity and feature stability deteriorate
Solution Approach 1:
The patent merges the feature extraction and high-order relation discovery functions into a unified graph neural network structure. Instead of using separate component feature extraction networks followed by relation modeling, the invention integrates both functions into a single stage where graph convolution operations simultaneously perform feature extraction and relation discovery, reducing overall network complexity while maintaining recognition precision.
Solution Approach 2:
The graph neural network structure serves multiple functions simultaneously: it extracts features from images, discovers high-order relations among components, and performs classification. This multi-functional approach eliminates the need for additional dedicated component feature extraction networks, as the graph neural network universally handles both feature extraction and relation modeling tasks.
2Loss of information
If additional component feature extraction networks are used, then feature representation may be enhanced, but feature stability and recognition effect worsen
Solution Approach 1:
The graph neural network performs self-service by automatically discovering high-order relations and generating stable feature representations without relying on additional external component feature extraction networks. The network uses its own embedded graph convolution operations to extract features and model relations, creating a self-sufficient feature extraction system that produces more stable and reliable representations.
Solution Approach 2:
The patent introduces graph structure as an intermediary representation that mediates between raw image features and final classification. The graph structure serves as a stable intermediate representation that captures high-order relations among image components, providing a reliable bridge that enhances feature stability while preserving important relational information for accurate recognition.
3Loss of information
If traditional multi-stage feature extraction is used, then comprehensive feature coverage is achieved, but processing time and computational complexity increase
Solution Approach 1:
The patent segments the feature extraction process into graph-based relational features and global pooling features, which are then integrated in a single stage. By dividing the feature representation into these distinct components that can be processed simultaneously through graph convolution and global pooling operations, the method achieves comprehensive feature coverage without requiring sequential multi-stage processing, thus reducing processing time.
Solution Approach 2:
The invention transitions from traditional hierarchical multi-stage feature extraction to a graph-based dimensional representation where features are organized by relational structure rather than processing stage. This dimensional change allows all necessary features to be extracted and integrated in parallel within a single stage, eliminating the temporal sequence of multiple stages while maintaining comprehensive feature coverage.
Data Source
AI summary
Embodiments of the present disclosure provides a fine-grained image recognition method and apparatus using graph structure represented high-order relation discovery, wherein the method includes: inputting an image to be classified into a convolutional neural network feature extractor with multiple stages, extracting two layers of network feature graphs in the last stage, constructing a hybrid high-order attention module according to the network feature graphs, and forming a high-order feature vector pool according to the hybrid high-order attention module, using each vector in the vector pool as a node, and utilizing semantic similarity among high-order features to form representative vector nodes in groups, and performing global pooling on the representative vector nodes to obtain classification vectors, and obtaining a fine-grained classification result through a fully connected layer and a classifier based on the classification vectors.