Abstract
In order to reduce the semantics gap and improve the consistency between structural difference among images and similarity of semantics from the corner of cognitive, many approaches on automatic image annotation have been developed vigorously. In addition to making use of both the features of n-order color moment and texture information, a multi-feature fusion method for automatic image annotation was proposed by using weighted histogram integral and closure regions counting in this paper. Based on Corel image data set, it showed in our experiments that the proposed approach can achieve better performance than that of traditional one using multi-label learning k-nearest neighbor algorithm, i.e., it can improve average precision measure index from 0.222 to 0.352 in automatic image annotation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amadasun, M., King, R.: Textural features corresponding to texture properties. IEEE Trans. Syst. Man Cybern. 19, 1264–1274 (1989)
Chang, T., Kuo, C.-C.J.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–441 (1993)
Zhang, Y.J.: Image Processing and Analyzing Technology. Higher Education Press, Beijing (2008)
Kong, X., Ng, M., Zhou, Z.H.: Transductive multilabel learning via label set propagation. IEEE Trans. Knowl. Data Eng. 25(3), 704–719 (2013)
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)
Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1075–1088 (2003)
Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007)
Zhang, M.L., Zhou, Z.H.: ML-kNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)
Schapire, R.E., Singer, Y.: BoosTexter: a boosting-based system for text categorization. Mach. Learn. 39(2/3), 135–168 (2000)
Elder, J.H., Zucker, S.W.: Computing contour closure. In: Buxton, B., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 399–412. Springer, Berlin Heidelberg (1996)
Acknowledgement
This work was funded by the National Science Foundation of China (No. 61472282, No. 61300058 and No. 61271098).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xia, S., Chen, P., Zhang, J., Li, XP., Wang, B. (2015). A Multi-feature Fusion Method for Automatic Multi-label Image Annotation with Weighted Histogram Integral and Closure Regions Counting. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-22053-6_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22052-9
Online ISBN: 978-3-319-22053-6
eBook Packages: Computer ScienceComputer Science (R0)