Abstract
Recent years have witnessed the effectiveness and efficiency of learning-based hashing methods which generate short binary codes preserving the Euclidean similarity in the original space of high dimension. However, because of their complexities and out-of-sample problems, most of methods are not appropriate for embedding of large-scale datasets. In this paper, we have proposed a new supervised hashing method to generate class-specific hash codes, which uses an inductive process based on the Inductive Manifold Hashing (IMH) model and leverage supervised information into hash codes generation to address these difficulties and boost the hashing quality. It is experimentally shown that this method gets excellent performance of image classification and retrieval on large-scale multimedia dataset just with very short binary codes.
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Zhang, M., Shen, F., Zhang, H., Xie, N., Yang, W. (2015). Hashing with Inductive Supervised Learning. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_45
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DOI: https://doi.org/10.1007/978-3-319-24078-7_45
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