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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Xinjing, W., Lei, Z., Feng, J., Weiying, M.: AnnoSearch: Image auto-annotation by search. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1483–1490. IEEE, Los Alamitos (2006)
Changhu, W., Feng, J., Lei, Z., Hongjiang, Z.: Image Annotation Refinement using Random Walk with Restarts. In: Proceedings of the 14th annual ACM international conference on Multimedia, pp. 647–650. ACM, Santa Barbara (2006)
Xinjing, W., Weiying, M., Lei, Z., Xing, L.: Iteratively clustering Web images based on link and attribute reinforcements. In: Proceedings of the 13th annual ACM international conference on Multimedia, pp. 122–131. ACM, Singapore (2005)
Bin, G., Tieyan, L., Tao, Q., Xin, Z., Qiansheng, C., Weiying, M.: Web image clustering by consistent utilization of visual features and surrounding texts. In: Proceedings of the 13th annual ACM international conference on Multimedia, pp. 112–121. ACM, Singapore (2005)
Feng, J., Changhu, W., Yuhuan, Y., Kefeng, D., Lei, Z., Weiying, M.: IGroup: Web Image Search Results Clustering. In: Proceedings of the 14th annual ACM international conference on Multimedia, pp. 377–384. ACM, Santa Barbara (2006)
Yohan, J., Latifur, K., Lei, W., Mamoun, A.: Image annotations by combining multiple evidence & wordNet. In: Proceedings of the 13th Annual ACM international Conference on Multimedia, pp. 706–715. ACM, Singapore (2005)
Keiji, Y., Kobus, B.: Finding Visual Concepts by Web Image Mining. In: Proceedings of the 15th international conference on World Wide Web, pp. 923–924. ACM, Edinburgh (2006)
Feng, K., Rong, J., Rahul, S.: Correlated Label Propagation with Application to Multi-label Learning. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1719–1726. IEEE, Los Alamitos (2006)
Mei, W., Xiangdong, Z., Tatseng, C.: Automatic image annotation via local multi-label classification. In: Proceedings of the 2008 international conference on Content-based image and video retrieval, pp. 17–26. ACM, Niagara Falls (2008)
Gulisong, N., Grigorios, T., Abbas, Z.K.: Clustering Based Multi-Label Classification for Image Annotation and Retrieval. IEEE, Los Alamitos (2009)
Nick, M., Chris, P., Randal, N.: Mining the Web for Visual Concepts. In: Proceedings of the 9th International Workshop on Multimedia Data Mining, pp. 18–25. ACM, Las Vegas (2008)
Florian, S., Antonio, C., Andrew, Z.: Harvesting image databases from the web. In: Proceedings of the 11th International Conference on Computer Vision, pp. 1–8. IEEE, Los Alamitos (2007)
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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
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