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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2015

01.08.2015 | Original Article

A novel image annotation model based on content representation with multi-layer segmentation

verfasst von: Jing Zhang, Yaxin Zhao, Da Li, Zhihua Chen, Yubo Yuan

Erschienen in: Neural Computing and Applications | Ausgabe 6/2015

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Abstract

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 .
Vorheriger Artikel The multi-attribute group decision-making method based on the interval grey uncertain linguistic generalized hybrid averaging operator
Nächster Artikel Adaptive functional-based neuro-fuzzy PID incremental controller structure

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Metadaten
Titel
A novel image annotation model based on content representation with multi-layer segmentation
verfasst von
Jing Zhang
Yaxin Zhao
Da Li
Zhihua Chen
Yubo Yuan
Publikationsdatum
01.08.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1815-6

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