A Hybrid Architecture Based on CNN for Image Semantic Annotation

Abstract : Due to semantic gap, some image annotation models are not ideal in semantic learning. In order to bridge the gap between cross-modal data and improve the performance of image annotation, automatic image annotation has became an important research hotspots. In this paper, a hybrid approach is proposed to learn automatically semantic concepts of images, which is called Deep-CC. First we utilize the convolutional neural network for feature learning, instead of traditional methods of feature learning. Secondly, the ensembles of classifier chains (ECC) is trained based on obtained visual feature for semantic learning. The Deep-CC corresponds to generative model and discriminative model, respectively, which are trained individually. Deep-CC not only can learn better visual features, but also integrates correlations between labels, when it classifies images. The experimental results show that this approach performs for image semantic annotation more effectively and accurately.
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Communication dans un congrès
9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. IFIP Advances in Information and Communication Technology, AICT-486, pp.81-90, 2016, Intelligent Information Processing VIII. 〈10.1007/978-3-319-48390-0_9〉
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Yongzhe Zheng, Zhixin Li, Canlong Zhang. A Hybrid Architecture Based on CNN for Image Semantic Annotation. 9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. IFIP Advances in Information and Communication Technology, AICT-486, pp.81-90, 2016, Intelligent Information Processing VIII. 〈10.1007/978-3-319-48390-0_9〉. 〈hal-01615004〉

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