Abstract
In this work, we propose a new image retrieval scheme by identifying better visual representations and fusing multiple similarities based on multiple features. For visual representation, we propose a new coarse-to-fine visual dictionary construction method based on the bag-of-features model. An adaptive soft assignment technique is developed to assign one local descriptor to several nearest visual words. To leverage the advantages of different features, a fusion strategy based on similarities is introduced to fuse multiple features. Experimental results demonstrate the effectiveness of the proposed method for image retrieval.
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Acknowledgements
This work was partially supported by the 973 Program (Project No. 2014CB347600), the National Natural Science Foundation of China (Grant No. 61402228, and 61702265) and the Natural Science Foundation of Jiangsu Province (Grant BK20140058, BK20170856 and BK20170033).
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Liu, H., Li, Z., Shu, X. (2018). Image Retrieval Based on Optimized Visual Dictionary and Adaptive Soft Assignment. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_18
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DOI: https://doi.org/10.1007/978-981-10-8530-7_18
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