Abstract
In this paper, the Relevance Feedback procedure for Content Based Image Retrieval is considered as an Exploration-Exploitation approach. The proposed method exploits the information obtained from the relevance score as computed by a Nearest Neighbor approach in the exploitation step. The idea behind the Nearest Neighbor relevance feedback is to retrieve the immediate neighborhood of the area of the feature space where relevant images are found. The exploitation step aims at returning to the user the maximum number of relevant images in a local region of the feature space. On the other hand, the exploration step aims at driving the search towards different areas of the feature space in order to discover not only relevant images but also informative images. Similar ideas have been proposed with Support Vector Machines, where the choice of the informative images has been driven by the closeness to the decision boundary. Here, we propose a rather simple method to explore the representation space in order to present to the user a wider variety of images. Reported results show that the proposed technique allows to improve the performance in terms of average precision and that the improvements are higher if compared to techniques that use an SVM approach.
Keywords
- Algorithms
- Active Learning
- max-min
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References
Information technology - Multimedia content description interface - Part 3: Visual, ISO/IEC Std. 15938-3:2003 (2003)
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: Identifying density-based local outliers. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) SIGMOD Conference, pp. 93–104. ACM (2000)
Cheng, J., Wang, K.: Active learning for image retrieval with co-svm. Pattern Recognition 40(1), 330–334 (2007)
Cohn, D.A., Atlas, L.E., Ladner, R.E.: Improving generalization with active learning. Machine Learning 15(2), 201–221 (1994)
Dasgupta, S., Hsu, D.: Hierarchical sampling for active learning. In: Cohen, W.W., McCallum, A., Roweis, S.T. (eds.) ICML. ACM International Conference Proceeding Series, vol. 307, pp. 208–215. ACM (2008)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 1–60 (2008)
Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: an experimental comparison. Inf. Retr. 11(2), 77–107 (2008)
Dorkó, G.: Selection of Discriminative Regions and Local Descriptors for Generic Object Class Recognition. Ph.D. thesis, Institut National Polytechnique de Grenoble (2006)
Giacinto, G.: A nearest-neighbor approach to relevance feedback in content based image retrieval. In: CIVR 2007: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 456–463. ACM, New York (2007)
Giacinto, G.: Moving targets in computer security and multimedia retrieval. Trans. MLDM 4(1), 30–52 (2011)
Giacinto, G., Roli, F.: Bayesian relevance feedback for content-based image retrieval. Pattern Recognition 37(7), 1499–1508 (2004)
Giacinto, G., Roli, F.: Instance-based relevance feedback for image retrieval. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 17, pp. 489–496. MIT Press (2005)
Gosselin, P.H., Cord, M.: Active learning methods for interactive image retrieval. IEEE Transactions on Image Processing 17(7), 1200–1211 (2008)
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Tech. Rep. 7694, California Institute of Technology (2007), http://authors.library.caltech.edu/7694
Hoi, S.C.H., Jin, R., Zhu, J., Lyu, M.R.: Semisupervised svm batch mode active learning with applications to image retrieval. ACM Trans. Inf. Syst. 27(3), 16:1–16:29 (2009)
Hoi, S.C.H., Lyu, M.R.: A semi-supervised active learning framework for image retrieval. In: CVPR (2), pp. 302–309. IEEE Computer Society (2005)
Huang, T., Dagli, C., Rajaram, S., Chang, E., Mandel, M., Poliner, G., Ellis, D.: Active learning for interactive multimedia retrieval. Proceedings of the IEEE 96(4), 648–667 (2008)
Jain, P., Kapoor, A.: Active learning for large multi-class problems. In: CVPR, pp. 762–769. IEEE (2009)
Jing, F., Li, M., Zhang, H., Zhang, B.: Entropy-based active learning with support vector machines for content-based image retrieval. In: ICME, pp. 85–88. IEEE (2004)
Katsavounidis, I., Jay Kuo, C.C., Zhang, Z.: A new initialization technique for generalized lloyd iteration. IEEE Signal Processing Letters 1(10), 144–146 (1994)
Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl. 2(1), 1–19 (2006)
Lindenbaum, M., Markovitch, S., Rusakov, D.: Selective sampling for nearest neighbor classifiers. Machine Learning 54(2), 125–152 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Lux, M., Chatzichristofis, S.A.: Lire: lucene image retrieval: an extensible java cbir library. In: MM 2008: Proceeding of the 16th ACM International Conference on Multimedia, pp. 1085–1088. ACM, New York (2008)
Pavlidis, T.: Limitations of content-based image retrieval (2008), http://theopavlidis.com/technology/CBIR/PaperB/vers3.htm
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: A database and web-based tool for image annotation. International Journal of Computer Vision 77(1-3), 157–173 (2008)
Sivic, J., Zisserman, A.: Efficient visual search for objects in videos. Proceedings of the IEEE 96(4), 548–566 (2008)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)
Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Systems, Man and Cybernetics 8(6), 460–473 (1978)
Tax, D.M.: One-class classification. Ph.D. thesis, Delft University of Technology, Delft, The Netherlands (June 2001)
Tong, S., Chang, E.Y.: Support vector machine active learning for image retrieval. In: ACM Multimedia, pp. 107–118 (2001)
Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001)
Wang, J., Hua, X.S.: Interactive image search by color map. ACM TIST 3(1), 12 (2011)
Wei, X.Y., Yang, Z.Q.: Coached active learning for interactive video search. In: Candan, K.S., Panchanathan, S., Prabhakaran, B., Sundaram, H., Chi Feng, W., Sebe, N. (eds.) ACM Multimedia, pp. 443–452. ACM (2011)
Winn, J.M., Criminisi, A., Minka, T.P.: Object categorization by learned universal visual dictionary. In: ICCV, pp. 1800–1807. IEEE Computer Society (2005)
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Piras, L., Giacinto, G., Paredes, R. (2012). Enhancing Image Retrieval by an Exploration-Exploitation Approach. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_28
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DOI: https://doi.org/10.1007/978-3-642-31537-4_28
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