Xin Luo
Shandong University
25 Papers
48 Citations
Xin Luo is an academic researcher from Shandong University. The author has contributed to research in topics: Hash function & Computer science. The author has an hindex of 10, co-authored 25 publications.
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Papers
SCRATCH: A Scalable Discrete Matrix Factorization Hashing Framework for Cross-Modal Retrieval
TL;DR: A novel supervised cross-modal hashing framework, namely Scalable disCRete mATrix faCtorization Hashing (SCRATCH), which utilizes collective matrix factorization on original features together with label semantic embedding, to learn the latent representations in a shared latent space.
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BATCH: A Scalable Asymmetric Discrete Cross-Modal Hashing
TL;DR: BATCH leverages collective matrix factorization to learn a common latent space for the labels and different modalities, and embeds the labels into binary codes by minimizing a distance-distance difference problem and introduces a quantization minimization term and orthogonal constraints into the optimization problem.
124
Fast Scalable Supervised Hashing
Xin Luo,Liqiang Nie,Xiangnan He,Ye Wu,Zhen-Duo Chen,Xin-Shun Xu +5 more
- 27 Jun 2018
TL;DR: A novel supervised hashing method, called Fast Scalable Supervised Hashing (FSSH), which circumvents the use of the large similarity matrix by introducing a pre-computed intermediate term whose size is independent with the size of training data.
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Discrete Hashing With Multiple Supervision
TL;DR: DSH supervises the hash code learning with both class-wise and instance-class similarity matrices, whose space cost is much less than the instance-pairwise similarity matrix, and outperforms some state-of-the-art methods.
75
Supervised Robust Discrete Multimodal Hashing for Cross-Media Retrieval
TL;DR: A new supervised cross-modal hashing method, named supervised robust discrete multimodal hashing (SRDMH), which incorporates full label information into the hash functions learning to preserve the similarity in the original space and becomes more robust and easier to solve by an iterative algorithm presented in this paper.
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