Image classification has been an incredibly active research topic in recent years with widespread applications. Researchers have put forward many remarkable techniques and semi-supervised learning (SSL) is one among them.… Click to show full abstract
Image classification has been an incredibly active research topic in recent years with widespread applications. Researchers have put forward many remarkable techniques and semi-supervised learning (SSL) is one among them. However, due to not taking the relationship of samples among different classes in consideration, previous approaches cannot often get a clear decision boundary. In this paper, we propose an improved classification model on the basis of SSL. First, we adopt a deformable part-based model to capture a stable global structure and salient objects, and then, we find a better decision boundary by our classification algorithm-based on an improved ensemble projection (IEP). Our IEP exploits the weighted average method. To evaluate the effectiveness of our approach, we do experiments not only with the LandUse-21 (L-21) data set, but also with an architecture style data set. Experimental results show that our approach is capable of achieving the state-of-the-art performance on the two data sets. For each class in L-21 data set, when 50 images are randomly chosen as training images, the multi-class average precision increases to 97.63%. Besides, for the architecture style data set, we achieve the best result with about 80% accuracy and have about a 10% improvement over the previous best work. Although there are a small number of labeled data used to train, we get the satisfactory performance.
               
Click one of the above tabs to view related content.