Abstract
In content-based image retrieval (CBIR), most techniques involve an important issue of how to efficiently bridge the gap between the high-level concepts and low-level visual features. We propose a novel semi-supervised learning method for image retrieval, which makes full use of both ICA feature and general low-level feature. Our approach can be characterized by the following three aspects: (1) The ICA feature, as proved to be representative of human vision, is adopted as a view to describe human perception; (2) A multi-view learning algorithm is introduced to make the most use of different features and dramatically reduce human relevance feedback needed to achieve a satisfactory result; (3) A new semi-supervised learning algorithm is proposed to adapt to the small sample problem and other special constrains of our multi-view learning algorithm. Our experimental results and comparisons are presented to demonstrate the effectiveness of the proposed approach.
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
Bell, A.J., Sejnowski, T.J.: An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)
Bell, A.J., Sejnowski, T.J.: The ’Independent Components’ of Natural Scenes are Edge Filters. Vision Research 37(23), 3327–3338 (1997)
Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-Training. In: Proc. of the Conference on Computational Learning Theory, pp. 92–100 (1998)
Cardoso, J.F., Laheld, B.: Equivariant Adaptive Source Separation. IEEE Trans. on Signal Processing 45(2), 434–444 (1996)
Hubel, D.: Eye, Brain, Vision. Scientific American Library (1995)
Hurri, J., Hyvarinen, A., Karhunen, J., Oja, E.: Image Feature Extraction Using Independent Component Analysis. In: Proc. IEEE Nordic Signal Processing Symp., Espoo, Findland, September 1996, pp. 475–478 (1996)
Hyvarinen, A.: A Family of Fixed-point Algorithm for Independent Component Analysis. In: Int. Conf. on Acoustic, Speech and Signal Processing, pp. 3917–3920 (1997)
Hyvarinen, A., Oja, E.: A Fast Fixed-point Algorithm for Independent Component Analysis. Neural Computation 9(7), 1483–1492 (1997)
Katsumata, N., Matsuyama, Y.: Similar-Image Retrieval Systems Using ICA and PCA Bases. In: Proc. of the International Joint Conference on Neural Networks, Montreal, Canada, July 31-August 4, 2005, pp. 1229–1334 (2005)
Lee, T.W., Lewicki, M.S., Sejnowski, T.: Unsupervised Classification with Non-Gaussian Mixture Models using ICA. In: Adavances in Nueral Information Processing Systems (1999)
Muslea, I., Minton, S., Knoblock, C.A.: Active + Semi-Supervised Learning = Robust Multi-View Learning. In: Proc. of the International Conference on Machine Learning, pp. 435–442 (2002)
Muslea, I., Minton, S., Knoblock, C.A.: Selective Sampling with Redundant View. In: Proc. of National Conf. on Artificial Intelligence, pp. 621–626 (2000)
Nigam, K., Ghani, R.: Analyzing the Effectiveness and Applicability of Co-Training. In: Proc. of Information and Knowledge Mnangement, pp. 86–93 (2000)
Olshausen, B.A., Field, D.J.: Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1? Vision Research 37(23), 3311–3325 (1997)
Puga, A.T.: A Computational Allegory for V1. In: Proc. of the 2nd International Symposium on Image and Signal Processing and Analysis, pp. 639–644 (2001)
Smeulders, A., Worring, M., Gupta, A., Jain, R.: Content-Based Image Retrieval at the End of the Early Years. Proc. IEEE Trans. Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Tieu, K., Viola, P.: Boosting Image Retrieval. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognitino, pp. 228–235 (2000)
Tong, S., Chang, E.: Support Vector Machine Active Learning for Image Retrieval. In: Proc. ACM Multimedia, Ottawa, Canada, pp. 107–118 (2001)
Vasconcelos, N., Lippman, A.: Learning From User Feedback in Image Retrieval Systems. In: Advances in Neural Information Processing Systems, Denver, Colorado (1999)
Vinje, W.E., Gallant, J.L.: Sparse Coding and Decorrelation in Primary Visual Cortex During Natural Vision. Science 287, 1273–1276 (2000)
Yan, R., Hauptmann, A.G.: Co-retrieval: a Boosted Reranking Approach for Video Retrieval. IEE Proc. Vision Image Signal Processing 152(6), 888–895 (2005)
Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B.: Learning with Local and Global Consistency. In: Advances in Neural Information Processing Systems, vol. 16 (2003)
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.: Ranking on data manifolds. In: Advances in Neural Information Processing Systems 16, MIT press, Cambridge (2004)
Zhou, X.S., Huang, T.S.: Comparing Discriminating Transformations and SVM for Learning during Multimedia Retrieval. In: Proc. ACM Multimedia, Ottawa, pp. 137–146 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, F., Dai, Q. (2006). A New Multi-view Learning Algorithm Based on ICA Feature for Image Retrieval. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_44
Download citation
DOI: https://doi.org/10.1007/978-3-540-69423-6_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69421-2
Online ISBN: 978-3-540-69423-6
eBook Packages: Computer ScienceComputer Science (R0)