Abstract
Typical biodiversity information systems can only solve a small part of user concerns. Available query mechanisms are based on traditional textual database manipulations, combmining them with spatial correlations. However, experts need more complex computations – e.g., using non-textual data sources. This involves a considerable amount of manual tasks, to obtain the needed information. This paper presents the specification and implementation of Sinimbu – a framework to process multimodal queries that support both text and images as search parameters, for biodiversity studies, thus providing support for subsequent complex simulations. Sinimbu was validated with real data from our university’s Zoology Museum, which houses one of the largest zoological museum collections in Brazil. Not only can users interact with the system in several modes, but query possibilities (and answers) vary according to the user’s profile. Query processing in Sinimbu combines work in database management, image processing and ontology construction and management.
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References
Addis, M.J., Boniface, M.J., Goodall, S., Grimwood, P., Kim, S.H., Lewis, P., Martinez, K., Stevenson, A.: SCULPTEUR: Towards a New Paradigm for Multimedia Museum Information Handling. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 582–596. Springer, Heidelberg (2003)
Amir, A., Berg, M., Permuter, H.: Mutual relevance feedback for multimodal query formulation in video retrieval. In: MIR 2005: Proc. of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 17–24 (2005)
Arpah, A., Alfred, S., Lim, L.H.S., Sarinder, K.K.S.: Monogenean image data mining using Taxonomy ontology. In: Int. Conf. on Networking and Information Technology (ICNIT), pp. 478–481 (2010)
Atnafu, S., Chbeir, R., Brunie, L.: Efficient content-based and metadata retrieval in image database. Journal of Universal Computer Science 8(6), 613–622 (2002)
Cullot, N., Parent, C., Spaccapietra, S., Vangenot, C.: Ontologies: A contribution to the DL/DB debate. In: Proc. of the 1st International Workshop on the Semantic Web and Databases, 29th VLDB Conf., pp. 109–129 (2003)
Torres, R.d.S., Falcão, A.X., Gonçalves, M.A., Papa, J.P., Zhang, B., Fan, W., Fox, E.A.: A genetic programming framework for content-based image retrieval. Pattern Recognition 42(2), 283–292 (2009)
Torres, R.d.S., Medeiros, C.B., Goncalves, M.A., Fox, E.A.: A Digital Library Framework for Biodiversity Information Systems. International Journal on Digital Libraries 6(1), 3–17 (2006)
Daltio, J., Medeiros, C.B.: Aondê: An Ontology Web Service for Interoperability across Biodiversity Applications. Information Systems 33, 724–753 (2008)
Daltio, J., Medeiros, C.B., Gomes Jr, L.C., Lewinsohn, T.: A Framework to Process Complex Biodiversity Queries. In: Proc. ACM Symposium on Applied Computing (ACM SAC) (March 2008)
GBIF. Global Biodiversity Information Facility Portal (2011), http://data.gbif.org/welcome.htm (accessed June 2011)
Guo, F., Li, L., Faloutsos, C., Xing, E.P.: C-dem: a multi-modal query system for drosophila embryo databases. In: Proc. VLDB Conference, vol. 1(2), pp. 1508–1511 (2008)
Huang, C.-B., Liu, Q.: An Orientation Independent Texture Descriptor for Image Retrieval. In: Int. Conf. on Communications, Circuits and Systems, ICCCAS, pp. 772–776 (2007)
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J., Zabih, R.: Image Indexing Using Color Correlograms. In: IEEE Conf. Computer Vision and Pattern Recognition, p. 762 (1997)
ICMI. International Conference on Multimodal Interaction (2011), http://www.acm.org/icmi/2011/
Song, H., Li, X., Wang, P.: Multimodal image retrieval based on annotation keywords and visual content. In: Proc. Int. Conf. on Control, Automation and Systems Engineering, pp. 295–298 (2009)
Stehling, R.O., Nascimento, M.A., Falcão, A.X.: A compact and efficient image retrieval approach based on border/interior pixel classification. In: Proc. 11th International Conf. on Information and Knowledge Management, CIKM 2002, pp. 102–109 (2002)
Su, J.-H., Wang, B.-W., Hsu, T.-Y., Chou, C.-L., Tseng, V.S.: Multi-modal image retrieval by integrating web image annotation, concept matching and fuzzy ranking techniques. International Journal of Fuzzy Systems 12(2), 136–149 (2010)
Tao, B., Dickinson, B.W.: Texture recognition and image retrieval using gradient indexing. Journal of Visual Communication and Image Representation 11(3), 327–342 (2000)
Vilar, B., Malaverri, J., Medeiros, C.B.: A Tool based on Web Services to Query Biodiversity Information. In: 5th International Conference on Web Information Systems and Technologies - WEBIST, pp. 305–310 (2009)
Williams, A., Yoon, P.: Content-based image retrieval using joint correlograms. Multimedia Tools and Applications 34, 239–248 (2007)
Zhang, B., Xiang, Q., Wang, Y., Shen, J.: CompositeMap: a novel music similarity measure for personalized multimodal music search. In: Proc. of the 17th ACM International Conference on Multimedia, MM 2009, pp. 973–974 (2009)
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de S. Fedel, G., Medeiros, C.B., dos Santos, J.A. (2012). Sinimbu – Multimodal Queries to Support Biodiversity Studies. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31125-3_47
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DOI: https://doi.org/10.1007/978-3-642-31125-3_47
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