Journal ArticleDOI
Semi-supervised constraints preserving hashing
Di Wang,Xinbo Gao,Xiumei Wang +2 more
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TLDR
A novel semi-supervised hashing method which preserves pairwise constraints for both low-dimensional embeddings and binary codes and can fully preserve pairwise semantic similarities for binary codes thus leading to better retrieval performance.About:
This article is published in Neurocomputing.The article was published on 2015-11-01. It has received 17 citations till now. The article focuses on the topics: Locality-sensitive hashing & Nearest neighbor search.read more
Citations
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Journal ArticleDOI
Robust and Flexible Discrete Hashing for Cross-Modal Similarity Search
Di Wang,Quan Wang,Xinbo Gao +2 more
TL;DR: A novel hashing model is proposed to efficiently learn robust discrete binary codes, which is referred as Robust and Flexible Discrete Hashing (RFDH), which is directly learned based on discrete matrix decomposition so that the large quantization error caused by relaxation is avoided.
Journal ArticleDOI
Multimodal Discriminative Binary Embedding for Large-Scale Cross-Modal Retrieval
TL;DR: The proposed MDBE can preserve both discriminability and similarity for hash codes, and will enhance retrieval accuracy, compared with the state-of-the-art methods for large-scale cross-modal retrieval task.
Journal ArticleDOI
Sequential Discrete Hashing for Scalable Cross-Modality Similarity Retrieval
TL;DR: This paper introduces a novel supervised cross-modality hashing framework, which can generate unified binary codes for instances represented in different modalities and significantly outperforms the state-of-the-art multimodality hashing techniques.
Journal ArticleDOI
Latent Structure Preserving Hashing
Li Liu,Mengyang Yu,Ling Shao +2 more
TL;DR: A more generalized multi-layer LSPH framework, in which hierarchical representations can be effectively learned by a multiplicative up-propagation algorithm, and Experimental results on three large-scale retrieval datasets show that ML-LSPH can achieve better performance than the single-layer lSPH and both of them outperform existing hashing techniques on large- scale data.
Journal ArticleDOI
Semi-Supervised Multi-View Discrete Hashing for Fast Image Search
Chenghao Zhang,Wei-Shi Zheng +1 more
TL;DR: This paper proposes a semi-supervised multi-view hash model that incorporates a portion of label information into the model and is able to minimize the loss jointly on multi-View features when using relaxation on learning hashing codes.
References
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Proceedings ArticleDOI
Object recognition from local scale-invariant features
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Journal ArticleDOI
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Aude Oliva,Antonio Torralba +1 more
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
Proceedings ArticleDOI
Locality-sensitive hashing scheme based on p-stable distributions
TL;DR: A novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p-stable distributions that improves the running time of the earlier algorithm and yields the first known provably efficient approximate NN algorithm for the case p<1.
Proceedings Article
Efficient sparse coding algorithms
TL;DR: These algorithms are applied to natural images and it is demonstrated that the inferred sparse codes exhibit end-stopping and non-classical receptive field surround suppression and, therefore, may provide a partial explanation for these two phenomena in V1 neurons.
Proceedings Article
Spectral Hashing
TL;DR: The problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be NP hard and a spectral method is obtained whose solutions are simply a subset of thresholded eigenvectors of the graph Laplacian.