Abstract
In this paper, we present an efficient deep supervised hashing method to learn robust hash codes for content-based image retrieval on large-scale datasets. Deep hashing methods have achieved some good results in image retrieval by training the network with classification loss and constructing hash functions as a latent layer. However, the classification loss does not impose a sufficient constraint on the network to make sure that similar images can be encoded to similar binary codes. As a supplement to classification loss, a new loss is delicately designed in our method. After trained with the joint objective functions, the network can generate more discriminative hash codes, which will increase the performance of retrieval. Our method outperforms the state-of-the-art methods by an obvious margin on three datasets CIFAR-10, CIFAR-100 and MNIST. Especially, the improvement is more impressive when the code length is short and the category number is large.
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Acknowledgment
This work was supported in part by the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing); and by Shenzhen International cooperative research projects GJHZ20170313150021171.
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Fan, J., Chen, C., Zhu, Y. (2018). Deep Discriminative Quantization Hashing for Image Retrieval. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_24
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DOI: https://doi.org/10.1007/978-3-030-00776-8_24
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