Improved deep semantic dictionary learning for multi-label image classification

Abstract

Multi-label image classification is a practical and challenging task in the field of machine learning. It is a fundamental but essential task used in auto driving, internet image classification, and other fields. Many researchers have done various researches on relevant aspects. Deep Semantic Dictionary Learning (DSDL) is a multi-label image classification method brought out by previous researchers which combines the semantic meaning information into the classification task. However, it only uses one kind of feature extraction network as the feature map generator without considering other alternatives. In this paper, six types of pretrained networks are applied as alternative choices to validate the performance of DSDL on different neural networks. Experiments are conducted on the VOC2007 dataset and the result shows the inner relation of different series of neural networks and demonstrates that different feature extraction networks perform variously on different tasks. The ResNet-152 achieves the mean average precision (mAP) of 94.5% and outperforms the original DSDL method of 0.3% mAP.