Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Categorizing Visual Contents by Matching Visual ``Keywords''
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Mental image search by boolean composition of region categories
Multimedia Tools and Applications
Generalized Fourier Descriptors with Applications to Objects Recognition in SVM Context
Journal of Mathematical Imaging and Vision
Fuzzy-rough data reduction with ant colony optimization
Fuzzy Sets and Systems
Efficient image concept indexing by harmonic & arithmetic profiles entropy
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Overview of the CLEF 2009 large-scale visual concept detection and annotation task
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
The University of Aamsterdam's concept detection system at ImageCLEF 2009
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Hidden semantic concept discovery in region based image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
MIRO'95 Proceedings of the Final conference on Multimedia Information Retrieval
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In this paper we propose an original image retrieval model inspired from the vector space information retrieval model. We build for different features and different scales a visual concept dictionary composed by visual words intended to represent a semantic concept, and then we represent an image by the frequency of the visual words within the image. Then the image similarity is computed as in the textual domain where a textual document is represented by a vector in which each component is the frequency of occurrence of a specific textual word in that document. We then adapt the common text-based paradigm by using the TF-IDF weighting scheme to construct a WF-IIF weighting scheme in our Multi-Scale Visual Dictionary (MSVD) vector space model. The experiments are conducted on the 2009 Visual Concept Detection ImageCLEF Campaign. We compare WF-IIF to usual direct Support-Vector Machine (SVM) algorithm. We demonstrate that SVM and WFIIF are in average over all the concept giving the same Area Under the Curve (AUC). We then discuss the fusion process that should enhance the whole system, and of some particular properties of MSVD, that shall be less dependant of the training set size of each concept than the SVM.