An Efficient Web Image Annotation Ranking Algorithm

Zheng Liu

doi:10.4028/www.scientific.net/AMR.108-111.81

Paper Titles

A Hierarchical Multicast Scheme for Heterogeneous Receivers
p.57

Image Registration Using Clustering Algorithm
p.63

A New 3D Medical Data Field Segmentation Algorithm Based on Improved K_Means Clustering
p.69

A Novel Transport Layer Protocol for Multi-Level Wireless Sensor Networks
p.74

An Efficient Web Image Annotation Ranking Algorithm
p.81

LDA-Based Automatic Image Annotation Model
p.88

Comparative Study of Nanyang Road Network by GIS
p.95

What Kind of Trait the Entrepreneur Needs to Possess?
p.100

A Geometry-Based Accelerated Fusion Clustering Algorithm and its Application in Marine Engineering
p.106

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 108-111An Efficient Web Image Annotation Ranking...

An Efficient Web Image Annotation Ranking Algorithm

Abstract:

Existing image annotation approaches mainly concentrate on achieving annotation results. Annotation order has not been taken into account carefully. As orderly annotation list could enhance the performance of image retrieval system, it is of great importance to rank annotations. This paper presents an algorithm to rank Web image annotating results. For an annotated Web image, we firstly partition the image by a region growing method. Secondly, relevance degree between two annotations is estimated through considering both semantic similarity and image content. Next, the regions of unlabeled image to be ranked serve as queries and annotations are used as the data points to be ranked. And then, manifold-ranking algorithm is executed to get the ordered annotation list. Experiments conducted on real-world Web images through NDCG metric demonstrate the effectiveness of the proposed approach.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 108-111)

Pages:

81-87

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.108-111.81

Citation:

Cite this paper

Online since:

May 2010

Authors:

Zheng Liu

Keywords:

Annotation Ranking, Manifold Ranking, NDCG Metric, Web Image Annotation

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] D. Liu, X. S. Hua, L. Yang, M. Wang, and H. J. Zhang. WWW"09, (2009), pp.351-360.

Google Scholar

[2] X. Rui, M. Li, Z. Li, W. -Y. Ma, and N. Yu: ACM MM"07, (2007), pp.585-594.

Google Scholar

[3] Wang, X.J., Zhang, L., Jing, F., Ma, W.Y.: CVPR"06, (2006) pp.1483-1490.

Google Scholar

[4] Hua, Z.G., Wang, X.J., Liu, Q.S., Lu, H.Q.: ACM MM"05, (2005), pp.467-470.

Google Scholar

[5] Y. A. Aslandogan and C. T. Yu. Diogenes: ACM MM"2000, (2000), pp.481-482.

Google Scholar

[6] Agarwal, S.: ICML"06, (2006), pp.25-32.

Google Scholar

[7] Rudi L. Cilibrasi, Paul M.B. Vitányi: IEEE Trans. on KDE, Vol. 19, No. 3, (2007), pp.370-383.

Google Scholar

[8] PhotoSIG: http: /www. photosig. com. Fig. 3 Average NDCG performance by different methods. Fig. 4 The percentage of images that have their most relevant annotation at different position.

Google Scholar

[9] D. Zhou, O. Bousquet, T. N. Lal, J. Weston and B. SchÖlkopf: Advances in Neural Information Processing Systems, vol. 16, (2004), pp.321-328.

Google Scholar

[10] D. Zhou, J. Weston, A. Gretton, O. Bousquet and B. SchÖlkopf: Advances in Neural Information Processing Systems vol. 16, (2004), pp.169-176.

Google Scholar

[11] D.G. Lowe: Int. J. Comput. Vision vol. 60(2), (2004), pp.91-110.

Google Scholar

[12] K. Jarvelin, and J. Kekalainen: ACM SIGIR"2000, (2000), pp.41-48.

Google Scholar

[13] Besl and Jain: IEEE Trans. on PAMI, Vol. 10, No. 2, (1988), pp.167-192.

Google Scholar

[14] Jianbo Shi and Jitendra Malik: IEEE Trans. on PAMI, Vol. 22, No. 8, (2000), pp.888-905.

Google Scholar