Abstract
Image annotation can significantly facilitate web image search and organization. Although it has been studied for years by the computer vision and machine learning communities, image annotation is still far from practical. Existing example-based methods are usually developed based on label co-occurrence information. However, due to the neglect of the associated label set’s internal correlation and relevance to image, the annotation results of previous methods often suffer from the problem of label ambiguity and noise, which limits the effectiveness of these labels in search and other applications. To solve the above problems, a novel model-free web image annotation approach is proposed in this paper, which consider both the relevance and correlation of the assigned label set. First, measures that can estimate the label set relevance and internal correlation are designed. Then, according to the above calculations, both factors are formulated into an optimization framework, and a search algorithm is proposed to find a label set as the final result, which reaches a reasonable trade-off between the relevance and internal correlation. Experimental results on benchmark web image data set show the effectiveness and efficiency of the proposed algorithm.
This work is supported by Scientific Research Fund of Heilongjiang Provincial Education Department(NO:12511011,12521055).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Makadia, A., Pavlovic, V., Kumar, S.: A new baseline for image annotation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 316–329. Springer, Heidelberg (2008)
Wang, X.: AnnoSearch: Image Auto-Annotation by Search. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1483–1490. IEEE Computer Society Press, Los Alamitos (2006)
Wang, X.-J.: Annotating images by mining image search results. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1919–1932 (2008)
Liu, D., Hua, X.-S., Yang, L.: Tag Ranking. In: ACM International Conference of World Wide Web, pp. 351–360. ACM Press, New York (2009)
Li, X.: Learning social tag relevance by neighbor voting. IEEE Transactions on Multimedia 11, 1310–1322 (2009)
Kang, F.: Correlated label propagation with application to multi-label learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1719–1726. IEEE Computer Society Press, Los Alamitos (2006)
Wang, H., Huang, H., Ding, C.H.Q.: Image annotation using multi-label correlated Greens function. In: International Conference on Computer Vision, pp. 1–8. IEEE Computer Society Press, Los Alamitos (2009)
Wang, H., Huang, H., Ding, C.: Multi-label Feature Transform for Image Classifications. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 793–806. Springer, Heidelberg (2010)
Chua, T.-S., Tang, J., Hong, R.: NUS-WIDE: A real-world web image database from National University of Singapore. In: ACM International Conference on Image and Video Retrieval, pp. 1–9. ACM Press, New York (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tian, F., Shen, X. (2013). Annotating Web Images by Combining Label Set Relevance with Correlation. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_76
Download citation
DOI: https://doi.org/10.1007/978-3-642-38562-9_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38561-2
Online ISBN: 978-3-642-38562-9
eBook Packages: Computer ScienceComputer Science (R0)