Research on efficiency optimization of nearest neighbor algorithms in image recognition

This study systematically analyzes the efficiency optimization of various nearest neighbor algorithms in image recognition. Through theoretical analysis and experimental comparisons, it is found that different algorithms perform differently across various data dimensions and scales: k-dimensional tree is suitable for low-dimensional data, Locality-Sensitive Hashing good scalability, Hierarchical Navigable Small World performs excellently on high-dimensional data but has high memory consumption, and Product Quantization achieves a balance between memory efficiency and query performance. T he research proposes targeted optimization strategies and establishes a comprehensive performance evaluation system, providing guidance for algorithm selection in practical applications. The findings are of great significance for improving the efficiency of nearest neighbor search in image recognition and lay the foundation for the widespread deployment of related technologies in real-world applications.