Graph Lattice Selective Learning for Image Classification

The patent pre-computes and stores graph lattices during an offline training phase, organizing subgraph features into a hierarchical lattice structure with confidence scores. This preliminary action allows the system to avoid expensive real-time graph matching computations during actual classification tasks, thereby reducing matching costs while maintaining robustness through the pre-organized feature hierarchy that accounts for noise and variability.

The system dynamically adapts the graph lattice structure based on training data characteristics, adjusting the hierarchy of subgraph features and their confidence scores. This dynamic adaptation allows the matching process to be more efficient by leveraging the pre-learning of data-specific patterns, reducing the computational burden during actual matching while maintaining robustness through the learned dynamic structure.

The patent segments the complex graph matching problem into a hierarchical lattice structure where subgraphs are organized by size and confidence scores. This segmentation breaks down the overwhelming task of matching large collections of subgraphs into manageable hierarchical levels, allowing the system to achieve high classification accuracy by progressively matching from smaller to larger subgraphs while controlling computational complexity through the segmented hierarchy.

The system changes the parameter of subgraph confidence scores to prioritize more reliable matches. By incorporating confidence scores that reflect the reliability of subgraph matches, the system can achieve high classification accuracy by focusing computational resources on high-confidence matches while filtering out low-confidence ones, thereby managing computational complexity effectively.