Abstract
Content-based image retrieval (CBIR) with global features is notoriously noisy, especially for image queries with low percentages of relevant images in a collection. Moreover, CBIR typically ranks the whole collection, which is inefficient for large databases. We experiment with a method for image retrieval from multimodal databases, which improves both the effectiveness and efficiency of traditional CBIR by exploring secondary modalities. We perform retrieval in a two-stage fashion: first rank by a secondary modality, and then perform CBIR only on the top-K items. Thus, effectiveness is improved by performing CBIR on a ‘better’ subset. Using a relatively ‘cheap’ first stage, efficiency is also improved via the fewer CBIR operations performed. Our main novelty is that K is dynamic, i.e. estimated per query to optimize a predefined effectiveness measure. We show that such dynamic two-stage setups can be significantly more effective and robust than similar setups with static thresholds previously proposed.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Aly, M., Welinder, P., Munich, M.E., Perona, P.: Automatic discovery of image families: global vs. local features. In: ICIP, pp. 777–780. IEEE, Los Alamitos (2009)
Arampatzis, A., Kamps, J., Robertson, S.: Where to stop reading a ranked list: threshold optimization using truncated score distributions. In: SIGIR, pp. 524–531. ACM, New York (2009)
Arampatzis, A., Robertson, S., Kamps, J.: Score distributions in information retrieval. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 139–151. Springer, Heidelberg (2009)
Barthel, K.U.: Improved image retrieval using automatic image sorting and semi-automatic generation of image semantics. In: International Workshop on Image Analysis for Multimedia Interactive Services, pp. 227–230 (2008)
Berber, T., Alpkocak, A.: DEU at ImageCLEFMed 2009: Evaluating re-ranking and integrated retrieval systems. In: CLEF Working Notes (2009)
Buckley, C., Voorhees, E.M.: Retrieval evaluation with incomplete information. In: SIGIR, pp. 25–32. ACM, New York (2004)
Chang, E., Goh, K., Sychay, G., Wu, G.: CBSA: content-based soft annotation for multimodal image retrieval using bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology 13(1), 26–38 (2003)
Chatzichristofis, S.A., Boutalis, Y.S., Lux, M.: Selection of the proper compact composite descriptor for improving content-based image retrieval. In: SPPRA, pp. 134–140 (2009)
Chatzichristofis, S.A., Boutalis, Y.S., Lux, M.: SpCD—spatial color distribution descriptor. A fuzzy rule based compact composite descriptor appropriate for hand drawn color sketches retrieval. In: ICAART, pp. 58–63 (2010)
Chatzichristofis, S.A., Arampatzis, A.: Late fusion of compact composite descriptors for retrieval from heterogeneous image databases. In: SIGIR, pp. 825–826. ACM, New York (2010)
Kilinc, D., Alpkocak, A.: Deu at imageclef 2009 wikipediamm task: Experiments with expansion and reranking approaches. In: Working Notes of CLEF (2009)
van Leuken, R.H., Pueyo, L.G., Olivares, X., van Zwol, R.: Visual diversification of image search results. In: WWW, pp. 341–350. ACM, New York (2009)
Lewis, D.D.: Evaluating and optimizing autonomous text classification systems. In: SIGIR, pp. 246–254. ACM Press, New York (1995)
Li, J., Wang, J.Z.: Real-time computerized annotation of pictures. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 985–1002 (2008)
Li, X., Chen, L., Zhang, L., Lin, F., Ma, W.Y.: Image annotation by large-scale content-based image retrieval. In: ACM Multimedia, pp. 607–610. ACM, New York (2006)
Maillot, N., Chevallet, J.P., Lim, J.H.: Inter-media pseudo-relevance feedback application to imageclef 2006 photo retrieval. In: CLEF Working Notes (2006)
Myoupo, D., Popescu, A., Le Borgne, H., Moellic, P.: Multimodal image retrieval over a large database. In: Peters, C., Caputo, B., Gonzalo, J., Jones, G.J.F., Kalpathy-Cramer, J., Müller, H., Tsikrika, T. (eds.) CLEF 2009. LNCS, vol. 6242, pp. 177–184. Springer, Heidelberg (2010)
Popescu, A., Moëllic, P.A., Kanellos, I., Landais, R.: Lightweight web image reranking. In: ACM Multimedia, pp. 657–660. ACM, New York (2009)
Popescu, A., Tsikrika, T., Kludas, J.: Overview of the wikipedia retrieval task at imageclef 2010. In: CLEF (Notebook Papers/LABs/Workshops) (2010)
Robertson, S.E., Hull, D.A.: The TREC-9 filtering track final report. In: TREC (2000)
Zagoris, K., Arampatzis, A., Chatzichristofis, S.A.: www.mmretrieval.net: a multimodal search engine. In: SISAP, pp. 117–118. ACM, New York (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arampatzis, A., Zagoris, K., Chatzichristofis, S.A. (2011). Dynamic Two-Stage Image Retrieval from Large Multimodal Databases. In: Clough, P., et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-20161-5_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20160-8
Online ISBN: 978-3-642-20161-5
eBook Packages: Computer ScienceComputer Science (R0)