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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6216))

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Abstract

Images are major source of Web content. Image annotation is an important issue which is adopted to retrieve images from large image collections based on the keyword annotations of images, which access a large image database with textual queries. With surrounding text of Web images increasing, there are generally noisy. So, an efficient image annotation approach for image retrieval is highly desired, which requires effective image search techniques. The developing clustering technologies allow the browsing and retrieval of images with low cost. Image search engines retrieved thousands of images for a given query. However, these results including a significant number of semantic noisy. In this paper, we proposed a new clustering algorithm Double-Circles that enable to remove noisy results and explicitly exploit more precise representative annotations. We demonstrate our approach on images collected from Flickr engine. Experiments conducted on real Web images present the effectiveness and efficiency of the proposed model.

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Zhou, T.H., Wang, L., Shon, H.S., Lee, Y.K., Ryu, K.H. (2010). Correlated Multi-label Refinement for Semantic Noise Removal. In: Huang, DS., Zhang, X., Reyes García, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_39

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  • DOI: https://doi.org/10.1007/978-3-642-14932-0_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14931-3

  • Online ISBN: 978-3-642-14932-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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