Multi-semantic Scene Classification Based on Region of Interest

Automatic semantic scene classification is a challenging research topic in computer vision and it is also a promising solution to scene understanding and image semantic retrieval. In this paper, novel techniques are proposed to implement multi-semantic scene classification. We first extract some regions of interest (ROIs) from each image based on image-driven, bottom-up visual attention model, and then propose two multi-instance multi-label learning algorithms, EMDD-SVM and EMDD-KNN to cope with this problem, where images are viewed as bags, each of which contains a number of instances corresponding to regions of interest and belongs to multiple categories simultaneously. Experimental results show that our ROIs extraction algorithm could obtain different kinds of interested objects effectively under various complex clutters and is highly tolerant to the noise, and that EMDD-SVM and EMDD-KNN algorithms have achieved good performance on multi-semantic scene classification by integrating multi-instance learning and multi-label learning.