Scene classification using local and global features with collaborative representation fusion
•A scene classification based on collaborative representation fusion is proposed.•The complementary nature of local and global spatial features is investigated.•Weighted fusion is designed based on residuals from two types of features.•Proposed LGF overcomes difficulties residing in feature or decis...
Saved in:
| Published in: | Information sciences 2016-06, Vol.348, p.209-226 |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | •A scene classification based on collaborative representation fusion is proposed.•The complementary nature of local and global spatial features is investigated.•Weighted fusion is designed based on residuals from two types of features.•Proposed LGF overcomes difficulties residing in feature or decision level fusion.
This paper presents an effective scene classification approach based on collaborative representation fusion of local and global spatial features. First, a visual word codebook is constructed by partitioning an image into dense regions, followed by the typical k-means clustering. A locality-constrained linear coding is employed on dense regions via the visual codebook, and a spatial pyramid matching strategy is then used to combine local features of the entire image. For global feature extraction, the method called multiscale completed local binary patterns (MS-CLBP) is applied to both the original gray scale image and its Gabor feature images. Finally, kernel collaborative representation-based classification (KCRC) is employed on the extracted local and global features, and class label of the testing image is assigned according to the minimal approximation residual after fusion. The proposed method is evaluated by using four commonly-used datasets including two remote sensing images datasets, an indoor and outdoor scenes dataset, and a sports action dataset. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods. |
|---|---|
| ISSN: | 0020-0255 1872-6291 |