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|Title:||A novel image tag saliency ranking algorithm based on sparse representation||Authors:||Wang, C.
tag saliency ranking
visual attention model
|Issue Date:||2013||Citation:||Wang, C.,Song, Z.,Feng, S.,Lang, C.,Yan, S. (2013). A novel image tag saliency ranking algorithm based on sparse representation. IEEE VCIP 2013 - 2013 IEEE International Conference on Visual Communications and Image Processing : -. ScholarBank@NUS Repository. https://doi.org/10.1109/VCIP.2013.6706420||Abstract:||As the explosive growth of the web image data, image tag ranking used for image retrieval accurately from mass images is becoming an active research topic. However, the existing ranking approaches are not very ideal, which remains to be improved. This paper proposed a new image tag saliency ranking algorithm based on sparse representation. we firstly propagate labels from image-level to region-level via Multi-instance Learning driven by sparse representation, which means reconstructing the target instance from positive bag via the sparse linear combination of all the instances from training set, instances with nonzero reconstruction coefficients are considered to be similar to the target instance; then visual attention model is used for tag saliency analysis. Comparing with the existing approaches, the proposed method achieves a better effect and shows a better performance. © 2013 IEEE.||Source Title:||IEEE VCIP 2013 - 2013 IEEE International Conference on Visual Communications and Image Processing||URI:||http://scholarbank.nus.edu.sg/handle/10635/83401||ISBN:||9781479902903||DOI:||10.1109/VCIP.2013.6706420|
|Appears in Collections:||Staff Publications|
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