Scene classification using local and global features with collaborative representation fusion

被引:138
作者
Zou, Jinyi [1 ]
Li, Wei [1 ]
Chen, Chen [2 ]
Du, Qian [3 ]
机构
[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
[2] Univ Texas Dallas, Dept Elect Engn, Richardson, TX 75080 USA
[3] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA
基金
中国国家自然科学基金;
关键词
Scene classification; Locality-constrained linear coding; Spatial pyramid matching; Collaborative representation-based classification; IMAGE; MODEL; SCALE;
D O I
10.1016/j.ins.2016.02.021
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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. (C) 2016 Elsevier Inc. All rights reserved,
引用
收藏
页码:209 / 226
页数:18
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