Image Recognition Based on Kernel Self-Optimized Learning

Image recognition technologies have been used in many areas, and feature extraction of image is key step for image recognition. A novel feature extraction method using kernel self-optimized learning for image recognition. The scheme of image feature extraction includes textural extraction using Gabor wavelet, textural features reduction based on class-wise locality preserving projection with the nearest neighbor graph and common kernel discriminant vector. The nearest neighbor classifier is applied to image classification. The feasibility and performance of the algorithm are testified in the public image databases.