Abstract
A novel Bag-of-Visual-Word (BoVW) based approach is developed in this paper to facilitate more effective Sketch-based Image Retrieval (SBIR). We focus on constructing the visual vocabulary based on the BoVW representation with both the spatial distribution and inter-relationship of descriptors. To optimize the sketch-image matching, the weighting quantization is created by integrating both the neighbor and spatial feature information to quantify features as visual words. We emphasize on an inverted indexing by converting an image to a trigram representation with visual words and their spatial information. Our experiments have obtained very positive results.
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Acknowledgements
This work is supported by National Natural Science Fund of China (No. 61572140), National Science & Technology Pillar Program of China (No. 2012BAH59F04), National Natural Science Fund of China (No. 61170095; 71171126), Shanghai Philosophy Social Sciences Planning Project (No. 2014BYY009), and Zhuoxue Program of Fudan University. Yuejie Zhang is the corresponding author.
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Jin, C., Li, C., Wang, Z., Zhang, Y., Zhang, T. (2016). Sketch-Based Image Retrieval with a Novel BoVW Representation. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_52
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DOI: https://doi.org/10.1007/978-3-319-27671-7_52
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