Image Object Category Recognition via Feature Extraction and Clustering

The patent extracts and retains only the most discriminative features from images rather than processing entire multi-layer frameworks. By identifying and extracting key feature points that are most relevant for classification, the system achieves accurate recognition while significantly reducing computational overhead and processing time.

The patent segments the image recognition process into distinct stages: feature extraction, clustering analysis, and classification. This segmentation allows the system to focus computational resources on the most critical operations rather than processing all data uniformly through deep multi-layer networks, thereby improving efficiency without sacrificing accuracy.

The system performs self-service by automatically learning common features directly from images without relying on pre-labeled training samples. The clustering algorithm autonomously identifies characteristic features of each object category, eliminating the need for manual annotation and reducing errors associated with training sample variations.

The patent changes the fundamental parameter approach by shifting from learning parameters through labeled training data to learning parameters directly from image content through clustering. This parameter transformation eliminates the uncertainty introduced by labeled data inconsistencies and achieves more reliable automatic classification.