Name
Abstract | ||
|---|---|---|
To extract salient features from images is significant for image classification. Deformable objects suffer from the problem that a number of pixels may have varying intensities. In other words, pixels at the same positions of training samples and test samples of an object usually have different intensities, which makes it difficult to obtain salient features of images of deformable objects. In this paper, we propose a novel method to address this issue. Our method first produces new representation of original images that can enhance pixels with moderate intensities of the original images and reduces the importance of other pixels. The new representation and original image of the object are complementary in representing the object, so the integration of them is able to improve the accuracy of image classification. The image classification experiments show that the simultaneous use of the proposed novel representations and original images can obtain a much higher accuracy than the use of only the original images. In particular, the incorporation of sparse representation with the proposed method can bring surprising improvement in accuracy. The maximum improvement in the accuracy may be greater than 8%. Moreover, The proposed non-parameter weighted fusion procedure is also attractive. The code of the proposed method is available at http://www.yongxu.org/lunwen.html. © 2015 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.patrec.2015.07.032 | Pattern Recognition Letters |
Keywords | Field | DocType |
Image classification,Image representation,Sparse representation | Computer vision,Pattern recognition,Image representation,Sparse approximation,Artificial intelligence,Pixel,Contextual image classification,Mathematics,Fusion procedure,Salient | Journal |
Volume | Issue | ISSN |
68 | P1 | 0167-8655 |
Citations | PageRank | References |
27 | 0.64 | 30 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Yong | 1 | 2119 | 73.51 |
| Bob Zhang | 2 | 728 | 69.17 |
| Zhong Zuofeng | 3 | 62 | 4.56 |