Attribute-based methods for image classification have received much attentions in recent years due to the high-level or human-specified nature of attributes. Given a new image, attribute-based methods can predict its category by exploiting the attribution representation of the given image. However, the foundation of attribute-based methods is predicting attributes precisely, which is still a difficult problem in real world applications. Therefore, in this paper, we propose an Attribute Prediction boosting framework with Matrix Factorization techniques APMF to boost the accuracy of attribute prediction. APMF explores the potential relationships of instances and attributes by utilizing the singular value decomposition SVD and non-negative matrix factorization NMF. A series of experiments show that our APMF achieves better attribute prediction accuracy than the state-of-the-art methods.
['Donghui Li', 'Zhuo Su', 'Hanhui Li', 'Xiaonan Luo']
Boosting Accuracy of Attribute Prediction via SVD and NMF of Instance-Attribute Matrix