Abstract
Manifold ranking (MR), as a powerful semi-supervised learning algorithm, plays an important role to deal with the relevance feedback problem in content-based image retrieval (CBIR). However, conventional MR has two main drawbacks: 1) in many cases, it is prone to exploit “unreliable” unlabeled images when deployed in CBIR due to the semantic gap; 2) the performance of MR is quite sensitive to the scale parameter used for calculating the Laplacian matrix. In this work, a self-immunizing MR approach is presented to address the drawbacks. Concretely, we first propose an elastic kNN graph as well as its constructing algorithm to exploit unlabeled images “safely”, and then develop a local scaling solution to calculate the Laplacian matrix adaptively. Extensive experiments on 10,000 Corel images show that the proposed algorithm is more effective than the state-of-the-art approaches.
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
Preview
Unable to display preview. Download preview PDF.
References
Chapelle, O., Scholkope, B., Zien, A.: Semisupervised Learning. MIT Press, Cambridge (2006)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of The New Age. ACM Comput. Surv. 40(2), 5:1-5:60 (2008)
He, J., Li, M., Zhang, H., Tong, H., Zhang, C.: Manifold-Ranking Based Image Retrieval. In: Proc. ACM Int. Conf. Multimedia, MM (2004)
Hoi, S.C.H., Jin, R., Zhu, J., Lyu, M.R.: Semi-Supervised SVM Batch Mode Active Learning for Image Retrieval. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, CVPR (2008)
Li, Y.F., Zhou, Z.H.: Towards Making Unlabeled Data Never Hurt. In: Proc. Int. Conf. Machine Learning, ICML (2011)
Luxberg, U.: A Tutorial on Spectral Clustering. Statistics and Computing 17(4), 395–416 (2007)
Wang, B., Pan, F., Hu, K.M., Paul, J.C.: Manifold-Ranking Based Retrieval using k-Regular Nearest Neighbor Graph. Pattern Recognition 45(4), 1569–1577 (2012)
Wu, J., Lin, Z., Lu, M.: Asymmetric Semi-Supervised Boosting for SVM Active Learning in CBIR. In: Proc. ACM Int. Conf. Image and Video Retrieval, CIVR (2010)
Wu, J., Lu, M., Wang, C.: Collaborative Learning between Visual Content and Hidden Semantic for Image Retrieval. In: Proc. IEEE Int. Conf. Data Mining, ICDM (2010)
Xu, B., Bu, J., Chen, C., Cai, D., He, X., Liu, W., Luo, J.: Efficient Manifold Ranking for Image Retrieval. In: Proc. ACM Int. Conf. Research and Development in Information Retrieval, SIGIR (2011)
Yang, Y., Nie, F., Xu, D., Luo, J., Zhuang, Y., Pan, Y.: A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback. IEEE Trans. Pattern Analysis and Machine Intelligence 34(4), 723–742 (2012)
Zelnik-Manor, L., Perona, P.: Self-Tuning Spectral Clustering. Adv. Neural. Inf. Process. Syst. (NIPS) 2 (2004)
Zhang, L., Wang, L., Lin, W.: Semisupervised Biased Maximum Margin Analysis for Interactive Image Retrieval. IEEE Trans. Image Processing 21(4), 2294–2308 (2012)
Zhou, X.S., Huang, T.S.: Relevance Feedback in Image Retrieval: A Comprehensive Review. Multimedia Syst. 8(6), 536–544 (2003)
Zhou, Z.H., Chen, K.J., Dai, H.B.: Enhancing Relevance Feedback in Image Retrieval using Unlabeled Data. ACM Transactions on Information Systems 24(2), 219–244 (2006)
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
Wu, J., Li, Y., Feng, S., Shen, H. (2013). A Self-immunizing Manifold Ranking for Image Retrieval. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-37456-2_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37455-5
Online ISBN: 978-3-642-37456-2
eBook Packages: Computer ScienceComputer Science (R0)