For representation based image classification methods, it is very important to well represent the target image. As pixels at same positions of training samples and test samples of an object usually have different inte...For representation based image classification methods, it is very important to well represent the target image. As pixels at same positions of training samples and test samples of an object usually have different intensities, it brings difficulty in correctly classifying the object. In this paper, we proposed a novel method to reduce the effects of this issue for image classification. Our method first produces a new representation （i.e. virtual image） of original image, which can enhance the importance of moderate pixel intensities and reduce the effects of larger or smaller pixel intensities. Then virtual images and corresponding original images are respectively used to represent a test sample and obtain two rep- resentation results. Finally, this method fuses these two results to classify the test sample. The integration of original image and its virtual image is able to improve the accuracy of image classification. The experiments of image classification show that the proposed method can obtain a higher accuracy than the conventional classification methods.展开更多
文摘For representation based image classification methods, it is very important to well represent the target image. As pixels at same positions of training samples and test samples of an object usually have different intensities, it brings difficulty in correctly classifying the object. In this paper, we proposed a novel method to reduce the effects of this issue for image classification. Our method first produces a new representation （i.e. virtual image） of original image, which can enhance the importance of moderate pixel intensities and reduce the effects of larger or smaller pixel intensities. Then virtual images and corresponding original images are respectively used to represent a test sample and obtain two rep- resentation results. Finally, this method fuses these two results to classify the test sample. The integration of original image and its virtual image is able to improve the accuracy of image classification. The experiments of image classification show that the proposed method can obtain a higher accuracy than the conventional classification methods.
