caa a22 4500 606159061 CHVBK 20210128100622.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00521-014-1815-6 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1815-6 A novel image annotation model based on content representation with multi-layer segmentation [Elektronische Daten] [Jing Zhang, Yaxin Zhao, Da Li, Zhihua Chen, Yubo Yuan] Image automatic annotation is an important issue of semantic-based image retrieval, and it is still a challenging problem for the reason of semantic gap. In this paper, a novel model with three parts is proposed. The first one is multi-layer image segmentation, in which saliency analysis and normalized cut are combined to segment images into semantic regions in the first layer. While in the second layer, the semantic regions are segmented into grids further . The second one is image content representation by region-based bag-of-words (RBoW) model, which is the variant of BoW model. Considering the correlations of labels, we adopt second-order CRFs as the third part of our model to ensure the accuracy of automatic image annotation. Experimental results show that our multi-layer segmentation-based image annotation model can achieve promising performance for multi-labeling and outperform the model based on single-layer segmentation and previous algorithm on Corel 5K and Pascal VOC 2007 datasets . The Natural Computing Applications Forum, 2015 Multi-label nationallicence Image content representation nationallicence Multi-layer segmentation nationallicence Bag-of-words nationallicence Conditional random fields nationallicence Image annotation nationallicence Zhang Jing Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut Zhao Yaxin Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut Li Da Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut Chen Zhihua Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut Yuan Yubo Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut Neural Computing and Applications Springer London 26/6(2015-08-01), 1407-1422 0941-0643 26:6<1407 2015 26 521 https://doi.org/10.1007/s00521-014-1815-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1815-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang Jing Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut NATIONALLICENCE 700 1- Zhao Yaxin Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut NATIONALLICENCE 700 1- Li Da Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut NATIONALLICENCE 700 1- Chen Zhihua Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut NATIONALLICENCE 700 1- Yuan Yubo Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, People's Republic of China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/6(2015-08-01), 1407-1422 0941-0643 26:6<1407 2015 26 521