Abstract
Effective image features are of vital importance for content-based image retrieval task. Recently, deep convolutional neural networks have been widely used in learning image features and have achieved promising results. However, there are still two questions need to be addressed. The first is the limitation of the image size in some works, and the second is the convolutional feature may not directly suitable for image retrieval. In our paper, we comprehensively solve these two problems by proposing a novel feature selection approach based on a pre-trained CNNs. Compared with others feature selection methods, our approach takes a two-stage strategy. The first stage is to select the effective feature sets using our proposed Median Sum Pooling Feature Selection method, and the second stage boosts the selected feature sets using the Space Channel Enhancement model. We evaluate our method on three benchmark datasets including Oxford5K, Paris6K, and Holiday. The experimental results show that our proposed method achieves competitive performance on both Oxford and Paris buildings benchmarks.
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Acknowledgments
This work was supported by National Nature Science Foundation of China (No. 61762025), Frontier and Application Foundation Research Program of CQ CSTC (No. cstc2017jcyjAX0340), The National Key Research and Development Program of China (No. 2017YFB1402400), Guangxi Key Laboratory of Trusted Software (No.kx201701), Guangxi Cooperative Innovation Center of Cloud Computing and Big Data (No.YD16E01), and Key Industries Common Key Technologies Innovation Projects of CQ CSTC (No. cstc2017zdcy-zdyxx0047), Chongqing Postdoctoral Science Foundation (No. Xm2017125), Social Undertakings and Livelihood Security Science and Technology Innovation Funds of CQ CSTC (No. cstc2017shmsA20013).
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Niu, Dd., Feng, Y., Shang, Jx., Qiang, Bh. (2018). SCE-MSPFS: A Novel Deep Convolutional Feature Selection Method for Image Retrieval. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_23
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