Abstract
It is very crucial for large-scale image retrieval tasks to extract effective hash feature representations. Encouraged by the recent advances in convolutional neural networks (CNNs), this paper presents a novel cascaded deep hashing (CDH) method to generate compact hash codes for highly efficient image retrieval tasks on given large-scale datasets. Specifically, we ingeniously utilize three CNN models to learn robust image feature representations on a given dataset, which solves the issue that categories with poor feature representation have a fairly low retrieval precision. Experimental results indicate that CDH outperforms some state-of-the-art hashing algorithms on both CIFAR-10 and MNIST datasets.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants No. 61373093, No. 61402310, No. 61672364 and No. 61672365, by the Soochow Scholar Project of Soochow University, by the Six Talent Peak Project of Jiangsu Province of China, by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. SJCX18_0846), and by the Graduate Innovation and Practice Program of colleges and universities in Jiangsu Province.
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Lu, J., Zhang, L. (2018). Cascaded Deep Hashing for Large-Scale 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_36
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