1
MSc. Student of computer Engineering, bu alisina University
2
Department of Computer Engendering , Bu-Ali Sina University
Abstract
Graph based semi-supervised methods for automatic image annotation are mainly focused on single-label problems. However, most of the real world problems require multiple labels per image. As a hybrid semi-supervised approach, LGC+ML-KNN is proposed for multi-label image annotation. LGC is a graph based semi-supervised learning algorithm that annotates unlabeled samples. Subsequently, ML-KNN learns from many more labeled samples, as compared to the initial training set. Experiments on several datasets confirm that the proposed approach has better accuracy than available methods, especially when a very small portion of the training set are the labeled samples.
Kordabadi, M., Mansoorizadeh, M., & Khotanlou, H. (2020). A graph based hybrid semi-supervised approach for automatic image annotation. Journal of Machine Vision and Image Processing, 6(2), 79-88.
MLA
Mojtaba Kordabadi; Muharram Mansoorizadeh; Hassan Khotanlou. "A graph based hybrid semi-supervised approach for automatic image annotation". Journal of Machine Vision and Image Processing, 6, 2, 2020, 79-88.
HARVARD
Kordabadi, M., Mansoorizadeh, M., Khotanlou, H. (2020). 'A graph based hybrid semi-supervised approach for automatic image annotation', Journal of Machine Vision and Image Processing, 6(2), pp. 79-88.
VANCOUVER
Kordabadi, M., Mansoorizadeh, M., Khotanlou, H. A graph based hybrid semi-supervised approach for automatic image annotation. Journal of Machine Vision and Image Processing, 2020; 6(2): 79-88.
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