Research on Efficient Image Feature Extraction based on Low-Rank Representation for Intelligent Reading Systems

• Published in: 2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) pp. 256 - 260
• Main Author: Tan, Yulan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.04.2024
• Subjects: Accuracy; Classification algorithms; computer science; Data mining; Feature extraction; Image feature extraction; intelligent reading systems; low-rank representation; Principal component analysis; Process control; Proposals; Rendering (computer graphics); Visualization; YOLO
• DOI: 10.1109/IPEC61310.2024.00051

Efficient and accurate extraction of image features is essential for ensuring high-performance information transmission in intelligent reading systems. This paper introduces a computer-assisted method for intelligent image feature extraction, specifically focusing on the Low-Rank Representation (LRR) approach. The study addresses challenges presented by noisy and diverse character or image data encountered in real-world scenarios. By employing LRR, the accuracy of image extraction, which includes object recognition and scene understanding, is significantly enhanced. Subsequently, the proposed techniques for intelligent image feature extraction undergo comprehensive evaluation, assessing both effectiveness and versatility through extensive experiment verifications. The experimental results validate the efficacy of the proposed method and demonstrate its superior image content extraction accuracy compared to existing methods. This proposed method provides crucial assistance to high-performance intelligent reading systems, thereby enhancing its applicability for potential consumers.