Abstract
In this paper, a novel fast support vector machine (SVM) method combining with the deep quasi-linear kernel (DQLK) learning is proposed for large scale image classification. This method can train large-scale dataset with SVM fast using less memory space and less training time. Since SVM classifiers are constructed by support vectors (SVs) that lie close to the separation boundary, removing the other samples that are not relevant to SVs has no effect on building the separation boundary. In other word, we need to reserve the boundary samples that are likely to be SVs. The proposed method uses an approximate separation classifier obtained by training a small subset selected from training data randomly as a reference to detect and remove non-relevant samples whose normalized algebraic distance to the reference classification boundary is larger than a threshold. The proposed method is implemented in the feature space. Therefore, by means of a good kernel method the proposed method can train high dimension data and image data. The DQLK method is used to extract and construct kernel matrix for the proposed method. Experimental results on different datasets and expended very large scale datasets show that the proposed method obtains outstanding ability to deal with very large scale image classification.
| Original language | English |
|---|---|
| Title of host publication | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1064-1071 |
| Number of pages | 8 |
| Volume | 2017-May |
| ISBN (Electronic) | 9781509061815 |
| DOIs | |
| Publication status | Published - 2017 Jun 30 |
| Event | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States Duration: 2017 May 14 → 2017 May 19 |
Other
| Other | 2017 International Joint Conference on Neural Networks, IJCNN 2017 |
|---|---|
| Country | United States |
| City | Anchorage |
| Period | 17/5/14 → 17/5/19 |
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ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Large-scale image classification using fast SVM with deep quasi-linear kernel. / Liang, Peifeng; Li, Weite; Liu, Donghang; Furuzuki, Takayuki.
2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May Institute of Electrical and Electronics Engineers Inc., 2017. p. 1064-1071 7965970.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Large-scale image classification using fast SVM with deep quasi-linear kernel
AU - Liang, Peifeng
AU - Li, Weite
AU - Liu, Donghang
AU - Furuzuki, Takayuki
PY - 2017/6/30
Y1 - 2017/6/30
N2 - In this paper, a novel fast support vector machine (SVM) method combining with the deep quasi-linear kernel (DQLK) learning is proposed for large scale image classification. This method can train large-scale dataset with SVM fast using less memory space and less training time. Since SVM classifiers are constructed by support vectors (SVs) that lie close to the separation boundary, removing the other samples that are not relevant to SVs has no effect on building the separation boundary. In other word, we need to reserve the boundary samples that are likely to be SVs. The proposed method uses an approximate separation classifier obtained by training a small subset selected from training data randomly as a reference to detect and remove non-relevant samples whose normalized algebraic distance to the reference classification boundary is larger than a threshold. The proposed method is implemented in the feature space. Therefore, by means of a good kernel method the proposed method can train high dimension data and image data. The DQLK method is used to extract and construct kernel matrix for the proposed method. Experimental results on different datasets and expended very large scale datasets show that the proposed method obtains outstanding ability to deal with very large scale image classification.
AB - In this paper, a novel fast support vector machine (SVM) method combining with the deep quasi-linear kernel (DQLK) learning is proposed for large scale image classification. This method can train large-scale dataset with SVM fast using less memory space and less training time. Since SVM classifiers are constructed by support vectors (SVs) that lie close to the separation boundary, removing the other samples that are not relevant to SVs has no effect on building the separation boundary. In other word, we need to reserve the boundary samples that are likely to be SVs. The proposed method uses an approximate separation classifier obtained by training a small subset selected from training data randomly as a reference to detect and remove non-relevant samples whose normalized algebraic distance to the reference classification boundary is larger than a threshold. The proposed method is implemented in the feature space. Therefore, by means of a good kernel method the proposed method can train high dimension data and image data. The DQLK method is used to extract and construct kernel matrix for the proposed method. Experimental results on different datasets and expended very large scale datasets show that the proposed method obtains outstanding ability to deal with very large scale image classification.
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UR - http://www.scopus.com/inward/citedby.url?scp=85030999903&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2017.7965970
DO - 10.1109/IJCNN.2017.7965970
M3 - Conference contribution
VL - 2017-May
SP - 1064
EP - 1071
BT - 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -