Semi-supervised Image Classification Learning Based on Random Feature Subspace

Image classification is a well-known classical problem in multimedia content analysis. In this paper a framework of semi-supervised image classification method is presented based on random feature subspace. Firstly, color spatial distribution entropy is introduced to represent the color spatial information, and texture feature are extracted by using Gabor filter. Then random subspaces of the feature vector are dynamically generated from mixed feature vector as different views. Finally, three classifiers are trained by the classified images and tri-training algorithm is applied to classify sample images. Experimental results strongly demonstrate the effectiveness and robustness of the proposed system.