Abstract
Local interest points serve as elementary building blocks in many image retrieval algorithms, and most of them exploit the local volume features using a Bag of Feature (BOF) model. However, the model ignores seriously valuable information about the global features in image and the distribution of the interest points. In this paper, we combine the sift feature and a global color feature. Then, we propose an improved strategy based on the BOF model. Finally, we embed the binary of the sift and color feature in the BOF model. Convincing experimental results on several datasets demonstrate that our proposed method approaches to the state-of-the-art level in image retrieval.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China Project No. 61671376, 11272253 and Natural Science Foundation of Shaanxi Province No. 2016JM6022.
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Tang, Z., Liao, K., Zheng, Y., Wang, W., Liu, M., Yuan, H. (2018). Image Retrieval Based on the Multi-index and Combination of Several Features. In: Zhao, P., Ouyang, Y., Xu, M., Yang, L., Ren, Y. (eds) Applied Sciences in Graphic Communication and Packaging. Lecture Notes in Electrical Engineering, vol 477. Springer, Singapore. https://doi.org/10.1007/978-981-10-7629-9_29
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DOI: https://doi.org/10.1007/978-981-10-7629-9_29
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