Classification Probability Estimation Based Multi-Class Image Retrieval
Aiming at multi-class large-scale image retrieval problem, a new image retrieval method based on classification probability estimation is proposed according to the thinking named “Classification First, Retrieval Later”. According to the method, the image features are effectively fused using a composite kernel method first, and a composite kernel classifier with higher classification precision is designed. The optimal coefficients of the classifier are also obtained utilizing the classification result with small-amount image samples. Second, complete the classification probability estimation for the testing images using the composite machine. Third, realize the image retrieval based on the classification probability estimation values. In the experiments with multi-class large-scale image dataset, it is confirmed that the presented method can achieve better retrieval precision. Moreover, the generalization performance without prior knowledge is also studied.
KeywordsComposite kernel Multiple kernel learning Support vector classifier Content-based image retrieval
This work was jointly supported by the National Natural Science Foundation for Young Scientists of China (Grant No: 61202332) and China Postdoctoral Science Foundation (Grant No: 2012M521905).
- 2.Datta R, Joshi D, Li J (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40:1–60 Google Scholar
- 3.Ravela S, Manmatha R (1997) Image retrieval by appearance. ACM SIGIR Philadephia PA, USA, pp 278–285Google Scholar
- 6.Content based image retrieval/image database search engine. http://wang.ist.psu.edu/docs/related/
- 7.Siggelkow S (2002) Feature histograms for content-based image retrieval. Ph.D.thesis, University of Freiburg, Institute for Computer Science, Freiburg, GermanyGoogle Scholar
- 12.Lewis DP, Jebara T, Noble WS (2006) Nonstationary kernel combination. In: Proceedings of the 23rd international conference on machine learning. ACM, Pittsburgh, USA, pp 553–560Google Scholar
- 13.Gönen M, Alpaydin E (2008) Localized multiple kernel learning. In: Proceedings of ICML’08, pp. 352–359Google Scholar
- 14.Kloft M, Brefeld U, Laskov P, Sonnenburg S (2008) Non-sparse multiple kernel learning. In: Proceedings of the NIPS workshop on kernel learning: automatic selection of optimal kernelsGoogle Scholar
- 16.Gehler PV, Nowozin S (2008) Infinite kernel learning. Technical report 178, Max Planck Institute for Biological Cybernetics, GermanyGoogle Scholar
- 18.Paolo P, Sandrine A, Eric D, Michel B (2009) Sparse multiscale patches (SMP) for image categorization. In: Proceedings of the 15th international multimedia modeling conference on advances in multimedia modeling, Sophia Antipolis, France (2009)Google Scholar