Chen Chen — Publications

J. Zou, W. Li, C. Chen, and Q. Du, “Scene Classification Using Local and Global Features with Collaborative Representation Fusion,” Information Sciences, vol. 348, pp. 209-226, June 2016. (doi: 10.1016/j.ins.2016.02.021)
  • Abstract:
    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. The typical $k$-means clustering method is utilized after constructing dense regions descriptors to generate the codebook. A locality-constrained linear coding is employed on the dense regions via the visual codebook, and a spatial pyramid matching strategy is then used to combine the 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 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 over four commonly-used datasets including two remote sensing images datasets, an indoor and outdoor scenes dataset, and a sports action events dataset. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods.

  • [PDF]