Abstract
Search algorithms in image retrieval tend to focus exclusively on giving the user more and more similar images based on queries that the user has to explicitly formulate. Implicitly, such systems limit the users exploration of the image space and thus remove the potential for serendipity. Thus, in recent years there has been an increased interest in developing exploration–exploitation algorithms for image search. We present an interactive image retrieval system that combines Reinforcement Learning together with a user interface designed to allow users to actively engage in directing the search. Reinforcement Learning is used to model the user interests by allowing the system to trade off between exploration (unseen types of image) and exploitation (images the system thinks are relevant).
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References
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47, 235–256 (2002)
Auer, P., Hussain, Z., Kaski, S., Klami, A., Kujala, J., Laaksonen, J., Leung, A.P., Pasupa, K., Shawe-Taylor, J.: Pinview: implicit feedback in content-based image retrieval. JMLR Workshop Conf. Proc. 11, 51–57 (2010)
Cox, I., Miller, M., Minka, T., Papathomas, T., Yianilos, P.: The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. Image Process. 9(1), 20–37 (2000)
Datta, R., Li, J., Wang, J.: Content-based image retrieval: approaches and trends of the new age. In: Multimedia Information Retrieval, pp. 253–262. ACM (2005)
Głowacka, D., Hore, S.: Balancing exploration-exploitation in image retrieval. In: Proceedings of UMAP 2014 Posters, Demonstrations and Late-Breaking Results (2014)
Głowacka, D., Shawe-Taylor, J.: Content-based image retrieval with multinomial relevance feedback. In: Proceedings of ACML, pp. 111–125 (2010)
Guiver, J., Snelson, E.: Learning to rank with softrank and Gaussian processes. In: Proceedings of SIGIR, pp. 259–266 (2008)
Kato, T., Kurita, T., Otsu, N., Hirata, K.: A sketch retrieval method for full color image database-query by visual example. In: Pattern Recognition. Computer Vision and Applications, pp. 530–533 (1992)
Kelly, D., Fu, X.: Elicitation of term relevance feedback: an investigation of term source and context. In: Proceedings of SIGIR (2006)
Kosch, H., Maier, P.: Content-based image retrieval systems-reviewing and benchmarking. JDIM 8(1), 54–64 (2010)
Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: Picsom-content-based image retrieval with self-organizing maps. Pattern Recogn. Lett. 21(13), 1199–1207 (2000)
Pham, T.-T., Maillot, N.E., Lim, J.-H., Chevallet, J.-P.: Latent semantic fusion model for image retrieval and annotation. In: Proceedings of CIKM (2007)
Piras, L., Giacinto, G., Paredes, R.: Enhancing image retrieval by an exploration-exploitation approach. In: Perner, P. (ed.) MLDM 2012. LNCS (LNAI), vol. 7376, pp. 355–365. Springer, Heidelberg (2012)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)
Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)
Suditu, N., Fleuret, F.: Iterative relevance feedback with adaptive exploration/exploitation trade-off. In: Proceedings of CIKM (2012)
Veltkamp, R.C., Tanase, M.: Content-based image retrieval systems: a survey. Department of Computing Science, Utrecht University (2002)
Villegas, M., Leiva, L.A., Paredes, R.: Interactive image retrieval based on relevance feedback. In: Sappa, A.D., Vitrià, J., Multimodal Interaction in Image and Video Application, vol. 48, pp. 83–109. Springer, Heidelberg (2013)
Yee, K.-P., Swearingen, K., Li, K., Hearst, M.: Faceted metadata for image search and browsing. In: Proceedings of CHI, pp. 401–408 (2003)
Zhou, X., Huang, T.: Relevance feedback in image retrieval: a comprehensive review. Multimed. Syst. 8(6), 536–544 (2003)
Acknowledgements
The project was supported by The Finnish Funding Agency for Innovation (under projects Re:Know and D2I) and by the Academy of Finland (under the Finnish Centre of Excellence in Computational Inference).
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Hore, S., Tyrvainen, L., Pyykko, J., Glowacka, D. (2014). A Reinforcement Learning Approach to Query-Less Image Retrieval. In: Jacucci, G., Gamberini, L., Freeman, J., Spagnolli, A. (eds) Symbiotic Interaction. Symbiotic 2015. Lecture Notes in Computer Science(), vol 8820. Springer, Cham. https://doi.org/10.1007/978-3-319-13500-7_10
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DOI: https://doi.org/10.1007/978-3-319-13500-7_10
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