Hierarchical Visual Pattern Classification via Local Feature Embedding

The patent segments the large number of visual pattern types into hierarchical classes and clusters. Instead of recognizing all patterns simultaneously, the system divides them into parent classes and child classes, allowing progressive recognition from general to specific. This segmentation reduces the computational burden at each stage while maintaining comprehensive recognition capability across all pattern types.

The patent introduces a hierarchical dimension to the recognition structure. By organizing patterns into multiple levels (parent classes, child classes, clusters), the system transforms a flat, single-dimensional recognition problem into a multi-dimensional hierarchical structure. This allows the system to handle increasing numbers of patterns by adding hierarchical depth rather than increasing computational complexity in the original dimension.

The patent segments the computational task into multiple stages corresponding to different hierarchical levels. Each stage handles a subset of patterns at its specific level, dividing the overall computational complexity into manageable chunks. This segmentation allows the system to scale comprehensiveness by adding hierarchical layers rather than increasing complexity linearly.

The patent performs preliminary actions by pre-organizing visual patterns into hierarchical classes and clusters before recognition. This pre-processing creates a structured framework that simplifies subsequent recognition operations. By establishing the hierarchical structure in advance, the system reduces the computational complexity required during actual recognition, as patterns are already grouped and organized for efficient processing.

The patent segments the classification process into hierarchical stages with multiple classifiers operating at different levels. Each classifier handles a specific subset of patterns at its level, and errors are contained within local clusters rather than propagating system-wide. This segmented approach maintains implementation simplicity while improving reliability through localized error containment and multiple verification stages.

The patent implements feedback mechanisms where classification results from parent classes inform child class classification, and misclassifications are corrected through auxiliary nodes and re-evaluation. This feedback loop allows the system to maintain high accuracy by continuously refining classifications based on results from previous hierarchical levels, while still using relatively simple classifier components at each stage.

The patent uses a nested hierarchical structure where new pattern classes can be added as child nodes within existing parent classes or as new parent classes at appropriate hierarchical levels. This nesting allows incremental expansion of comprehensiveness without requiring complete reprocessing of all patterns. New classes are integrated into the existing hierarchical framework, minimizing the computational cost of adding comprehensiveness.

The patent performs preliminary organization of patterns into hierarchical classes and clusters before recognition. When new pattern classes are added, they can be pre-integrated into the hierarchical structure, and the pre-computed hierarchical relationships can be leveraged to minimize additional computational cost. This preliminary structuring allows the system to accommodate new classes efficiently without linearly increasing computational requirements.