基于无字典模型的红外与可见光图像融合分类

In view of the shortcomings and limitations of traditional image classification algorithms based on a single image source, a novel image classification method based on infrared image and visible image fusion is proposed; this combines the advantages of infrared imaging and visible imaging, and thus makes the feature information much more abundant. In the first step, sets of dense scale invariant feature transform (D-SIFT ) features were extracted from the infrared imaging and visible images. Then, an effective codebookless model (CLM) was employed, along with a spatial pyramid matching (SPM) strategy, which divides an image into regular regions. Finally, a composite kernel method based on support vector machines (SVM-CK) was utilized to fuse the extracted features and realize the final classification. The proposed method was validated using VAIS and RGB-NIR databases. The accuracy of fusion were both improved than that obtained using a single image source. Compared with traditional methods such as BoVW, the accuracy is improved by 4.7% and 12%, respectively. The experimental results show that the results of multidata fusion are significantly better than those based on single data sources.