Neighborhood Feature Line Segment for Image Classification

In this paper, two improved classifiers based on the nearest feature line (NFL) are proposed for image classification, where the neighborhood feature line segment-I (NFLS-I) classifier uses the neighborhood of prototypes to select the better-fitted feature lines (FLs) and the neighborhood feature line segment-II (NFLS-II) classifier utilizes the neighborhood of the query sample to choose more-likely FLs. With better selection of FLs, these two classifiers can both improve the recognition performance and the computation problem. A large number of experiments on Soil-100 object database, Yale face database, FEI face database, AR face database, and Jochen triesch static hand posture database are performed to evaluate these two proposed classifiers. The experimental results demonstrate that the proposed NFLS-I and NFLS-II classifiers outperform the original NFL and some other improved NFL classifiers for object recognition, hand posture recognition, and face recognition.