Abstract
With the exponential increase of image database, Content Based Image Retrieval research field has started a race to always propose more effective and efficient tools to manage massive amount of data. In this paper, we focus on improving the discriminative power of the well-known bag of visual words model. To do so, we present n-BoVW, an approach that combines visual phrase model effectiveness keeping the efficiency of visual words model with a binary based compression algorithm. Experimental results on widely used datasets (UKB, INRIA Holidays, Corel1000 and PASCAL 2012) show the effectiveness of the proposed approach.
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Acknowledgments
This research is supported by the Poitou-Charentes Regional Founds for Research activities and the European Regional Development Founds (ERDF) inside the e-Patrimoine project from the axe 1 of the NUMERIC Program.
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Ouni, A., Urruty, T., Visani, M. (2017). Improving the Discriminative Power of Bag of Visual Words Model. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10133. Springer, Cham. https://doi.org/10.1007/978-3-319-51814-5_21
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DOI: https://doi.org/10.1007/978-3-319-51814-5_21
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