Hierarchical Feature Clustering for Robust Pattern Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing pattern recognition methods are ineffective in extracting features that are robust across different categories of objects and are not robust to changes in size, direction, or other transformations, limiting their applicability to diverse recognition tasks.
Innovation Solution
A learning method and device that detects local features with geometric structures, performs clustering, selects representative features, and uses a learning dataset with these features for supervised learning to enhance recognition and detection across multiple categories while minimizing the influence of changes in the subject.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If clustering by vector quantization is applied to extract local features, then a few useful features can be extracted, but the extracted features are effective only for specific subjects and not for detection and recognition of subjects in other categories
Solution Approach 1:
The patent segments the feature extraction process into multiple hierarchical levels. First, local features are extracted from individual images, then clustered to form basic feature units. These units are further combined to form higher-level features, creating a hierarchical structure that enables features to be both specific (at lower levels) and generalizable (at higher levels) across different subject categories.
Solution Approach 2:
The patent introduces a new dimension of hierarchical organization beyond traditional single-level feature extraction. By organizing features into multiple levels (local features → basic feature units → higher-level features), the system transforms the feature representation space, enabling features to capture both fine-grained details and coarse-grained patterns that generalize across categories.
2Reliability
If traditional recognition algorithms are used, then image recognition can be performed, but they are not robust to changes in size, direction or other transformations of the subject
Solution Approach 1:
The patent creates a universal feature representation system where the same hierarchical feature extraction and clustering process can be applied to any subject category. The learned feature units and their combination rules are category-agnostic, making the system universally applicable while maintaining robustness to transformations through the hierarchical structure that captures invariant patterns.
Solution Approach 2:
The patent changes the parameters of feature representation by organizing features hierarchically and using clustering to form discrete feature units. This transformation of the feature space parameters enables the system to represent subjects in a way that is invariant to size, direction, and other transformations, while still maintaining category-specific discriminative power.
3Measurement precision
If more intricate features combining local features are extracted, then recognition accuracy can be improved, but the complexity of the learning process increases
Solution Approach 1:
The patent performs preliminary clustering of local features to form basic feature units before combining them into higher-level features. This preliminary organization simplifies the subsequent learning process by providing a structured foundation of reusable feature units, reducing the complexity of learning intricate combinations while maintaining high recognition accuracy.
Solution Approach 2:
The clustering process automatically organizes local features into meaningful units without requiring manual specification of feature combinations. The system self-organizes the feature space through clustering, and the learned feature units automatically serve as building blocks for higher-level features, reducing the need for complex manual feature engineering.
Data Source
AI summary
In learning for pattern recognition, an aggregation of different types of object image data is inputted, and local features having given geometric structures are detected from each object image data inputted. The detected local features are put through clustering, plural representative local features are selected based on results of the clustering, and a learning data set containing the selected representative local features as supervisor data is used to recognize or detect an object that corresponds to the object image data. The learning thus makes it possible to appropriately extract, from an aggregation of images, local features useful for detection and recognition of subjects of different categories.