Large-scale image classification using fast SVM with deep quasi-linear kernel

Peifeng Liang, Weite Li, Donghang Liu, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1064-1071
Number of pages8
Volume2017-May
ISBN (Electronic)9781509061815
DOIs
Publication statusPublished - 2017 Jun 30
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 2017 May 142017 May 19

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period17/5/1417/5/19

Fingerprint

Image classification
Support vector machines
Classifiers
Data storage equipment

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Liang, P., Li, W., Liu, D., & Furuzuki, T. (2017). Large-scale image classification using fast SVM with deep quasi-linear kernel. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (Vol. 2017-May, pp. 1064-1071). [7965970] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2017.7965970

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 proceedingConference contribution

Liang, P, Li, W, Liu, D & Furuzuki, T 2017, Large-scale image classification using fast SVM with deep quasi-linear kernel. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. vol. 2017-May, 7965970, Institute of Electrical and Electronics Engineers Inc., pp. 1064-1071, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 17/5/14. https://doi.org/10.1109/IJCNN.2017.7965970
Liang P, Li W, Liu D, Furuzuki T. Large-scale image classification using fast SVM with deep quasi-linear kernel. In 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 https://doi.org/10.1109/IJCNN.2017.7965970
Liang, Peifeng ; Li, Weite ; Liu, Donghang ; Furuzuki, Takayuki. / Large-scale image classification using fast SVM with deep quasi-linear kernel. 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1064-1071
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