A multi-label classification model using convolutional netural networks
Guanglei Zhang, Lei Chen, Yongsheng Ding · 2017
In this paper, a novel multi-label classification model using convolutional neural networks (CNNs) is proposed. As one of the deep learning architectures, CNNs lead breakthrough in many fields of image processing especially the image classification. Since the applications of CNNs are more concentrating in the background of single-label samples, our model introduce the hidden semantic between different labels of the same sample to the existed CNNs to enhance the performance. In order to use the semantic of the multi-label, i.e. fine-label and coarse-label, a coarse-label classification part was built using the shared low features of the fine-label classification. We have compared our method with the CNNs using single label. Experimental results demonstrate that our model can achieve better classification performance on the multi-label dataset of CIFAR-100 than the CNNs using single label, our model improves the classification performance, by 2.3% increasing for the top-1 accuracy, while 2.7% for the top-5 on average.