Open AccessProceedings Article
A multi-label least-squares hashing for scalable image search
Shengsheng Wang,Zi Huang,Xin-Shun Xu +2 more
- pp 954-962
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TLDR
A Multi-label Least-Squares Hashing (MLSH) method for multi-label data hashing, which outperforms several state-of-the-art hashing methods including supervised and unsupervised methods.Abstract:
Recently, hashing methods have attracted more and more attentions for their effectiveness in large scale data search, e.g., images and videos data. etc. For different s-cenarios, unsupervised, supervised and semi-supervised hashing methods have been proposed. Especially, when semantic information is available, supervised hashing methods show better performance than unsupervised ones. In many practical applications, one sample usually has more than one label, which has been considered by multi-label learning. However, few supervised hashing methods consider such scenario. In this paper, we propose a Multi-label Least-Squares Hashing (MLSH) method for multi-label data hashing. It can directly work well on multi-label data; moreover, unlike other hashing methods which directly learn hashing function-s on original data, MLSH first utilizes the equivalen-t form of CCA and Least-Squares to project original multi-label data into lower-dimensional space; then, in the lower-dimensional space, it learns the project matrix and gets final binary codes of data. MLSH is tested on NUS-WIDE and CIFAR-100 which are widely used for searching task. The results show that MLSH outperforms several state-of-the-art hashing methods including supervised and unsupervised methods.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Proceedings ArticleDOI
Supervised Robust Discrete Multimodal Hashing for Cross-Media Retrieval
TL;DR: A novel supervised hashing framework for cross-modal retrieval, i.e., Supervised Robust Discrete Multimodal Hashing (SRDMH), which tries to make final binary codes preserve label information as same as that in original data so that it can leverage more label information to supervise the binary codes learning.
Proceedings ArticleDOI
Discrete Multi-view Hashing for Effective Image Retrieval
Rui Yang,Yuliang Shi,Xin-Shun Xu +2 more
TL;DR: A novel hashing method, i.e., Discrete Multi-view Hashing (DMVH), which can work on multi-view data directly and make full use of rich information in multi-View data, and a novel approach to construct similarity matrix, which can not only preserve local similarity structure, but also keep semantic similarity between data points.
Proceedings ArticleDOI
Dictionary Learning Based Hashing for Cross-Modal Retrieval
TL;DR: DLCMH learns dictionaries and generates sparse representation for each instance, which is more suitable to be projected to latent space and outperforms or is comparable to several state-of-the-art hashing models.
Journal ArticleDOI
Linear unsupervised hashing for ANN search in Euclidean space
TL;DR: An unsupervised hashing method - Unsupervised Euclidean Hashing (USEH), which learns and generates hashing codes to preserve the Euclidan distance relationship between data and is comparable to state-of-the-art unsuper supervised hashing methods.
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