J3S Model

Description

We propose a Joint Statistical and Spatial Sparse representation models the image or image-set data for classification, by reconciling both their local patch structures and global Gaussian distribution mapped into Riemannian manifold.

J3S model can be considered as the joint sparse coding problem and solved by coupling the local and global image representations using joint sparsity. The learned J3S models are used for robust image and image-set classification.

Usage

Here we take UIUC-material database as an example.

Usage: run J3S.m

Experiments results over ETH-80, FMD, UIUC, and YTC databases.

Method	ETH-80	FMD	UIUC	YTC
AHISD	72.50	46.72	55.37	64.65
CHISD	79.75	47.52	65.09	67.24
MMD	85.75	60.60	62.78	69.60
MDA	87.75	62.50	67.13	64.72
SPDML-AIRM	90.75	63.42	74.72	67.50
SPDML-SETIN	90.75	63.80	68.24	68.10
LEML	93.50	66.60	69.17	69.85
RMML-SPD	95.00	68.88	70.09	78.05
RMML-GM	93.00	69.62	76.48	69.15
CDL-LDA	94.00	76.92	78.89	70.21
CDL-PLS	94.00	75.36	76.39	69.94
RSR	91.50	74.92	72.59	72.77
KGDL	93.00	77.40	76.32	73.91
Liu et al.	92.5	N/A	N/A	78.8
DRM	98.12	N/A	N/A	72.55
DMK	96.8	N/A	N/A	80.3
MMDML	94.50	N/A	N/A	78.5
J3S w/o Sp Dict.	97.75	82.12	84.03	83.34
J3S	98.25	82.36	85.65	83.40

Citation

Paper is available here.

In case of use, please cite our paper:

H. Cheng, and B. Wen. "Joint Statistical and Spatial Sparse Representation for Robust Image and Image-Set Classification." 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020.

Bibtex is here:

  @inproceedings{cheng2020joint,
    title={Joint Statistical and Spatial Sparse Representation for Robust Image and Image-Set Classification},
    author={Cheng, Hao and Wen, Bihan},
    booktitle={2020 IEEE International Conference on Image Processing (ICIP)},
    pages={2411--2415},
    year={2020},
    organization={IEEE}
  }

Contact

If you have any questions or suggestions, please contact me

HAO006@ntu.edu.sg