A compact and efficient image retrieval approach based on border/interior pixel classification
Proceedings of the eleventh international conference on Information and knowledge management
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Mutual relevance feedback for multimodal query formulation in video retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Content-based image retrieval using joint correlograms
Multimedia Tools and Applications
A framework to process complex biodiversity queries
Proceedings of the 2008 ACM symposium on Applied computing
A genetic programming framework for content-based image retrieval
Pattern Recognition
C-DEM: a multi-modal query system for Drosophila Embryo databases
Proceedings of the VLDB Endowment
Multimodal Image Retrieval Based on Annotation Keywords and Visual Content
CASE '09 Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009)
CompositeMap: a novel music similarity measure for personalized multimodal music search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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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.