Incremental Image Classification Method Based on Semi-Supervised Learning

In order to use large numbers of unlabeled images effectively,an image classification method is proposed based on semi-supervised learning.The proposed method bridges a large amount of unlabeled images and limited numbers of labeled images by exploiting the common topics.The classification accuracy is improved by using the must-link constraint and cannot-link constraint of labeled images.The experimental results on Caltech-101 and 7-classes image dataset demonstrate that the classification accuracy improves about 10% by the proposed method.Furthermore,due to the present semi-supervised image classification methods lacking of incremental learning ability,an incremental implementation of our method is proposed.Comparing with non-incremental learning model in literature,the incremental learning method improves the computation efficiency of nearly 90%.