Abstract
Learning to hash embeds objects (e.g. images/documents) into a binary space with the semantic similarities preserved from the original space, which definitely benefits large-scale tough tasks such as image retrieval. By leveraging semantic labels, supervised hashing methods usually achieve better performance than unsupervised ones in real-world scenarios. However, most existing supervised methods do not sufficiently encourage inter-class separability and intra-class compactness which is quite crucial in discriminative hashcodes. In this paper, we propose a novel hashing method called Large-Margin Supervised Hashing (LMSH) based on a non-linear classification framework. Specifically, LMSH introduces the angular decision margin which could adjust inter-class separability and intra-class compactness through a hyper-parameter for more discriminative codes. Extensive experiments on three public datasets are conducted to demonstrate the LMSH’s superior performance to some state-of-the-arts in image retrieval tasks.
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Zhang, X., Zhang, H., Chen, Y., Liu, X. (2017). Large-Margin Supervised Hashing. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_28
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DOI: https://doi.org/10.1007/978-3-319-70087-8_28
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