A multi-label image annotation scheme based on improved SVM multiple kernel learning

Cong Jin; Shu-Wei Jin

doi:10.1117/12.2266104

8 February 2017 A multi-label image annotation scheme based on improved SVM multiple kernel learning

Cong Jin, Shu-Wei Jin

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 1022510 (2017) https://doi.org/10.1117/12.2266104
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

Multi-label image annotation (MIA) has been widely studied during recent years and many MIA schemes have been proposed. However, the most existing schemes are not satisfactory. In this paper, an improved multiple kernel learning (IMKL) method of support vector machine (SVM) is proposed to improve the classification accuracy of SVM, then a novel MIA scheme based on IMKL is presented, which uses the discriminant loss to control the number of top semantic labels, and the feature selection approach is also used for improving the performance of MIA. The experiment results show that proposed MIA scheme achieves higher the performance than the existing other MIA schemes, its performance is satisfactory for large image dataset.

Citation Download Citation

Cong Jin and Shu-Wei Jin "A multi-label image annotation scheme based on improved SVM multiple kernel learning", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022510 (8 February 2017); https://doi.org/10.1117/12.2266104

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