Label Consistent Matrix Factorization Hashing for Large-Scale Cross-Modal Similarity Search
Label Consistent Matrix Factorization Hashing for Large-Scale Cross-Modal Similarity Search
复制标题
为大规模跨模式相似性搜索标记一致矩阵分解散列
DOI：
10.1109/tpami.2018.2861000
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发表时间：
2019-10
期刊：
影响因子：
--
通讯作者：
Lihuo He
中科院分区：
文献类型：
--
作者：
Di Wang;Xinbo Gao;Xiumei Wang;Lihuo He
多模态散列算法以其高效性和有效性在大规模多媒体数据集上的跨模态相似性搜索中引起了广泛的关注。最近，监督的多模态哈希，试图保持从训练数据的标签获得的语义信息，受到了相当大的关注，其较高的搜索精度相比，无监督的多模态哈希。虽然这些算法是有前途的，他们主要是为了保持成对的相似性。在给定训练数据的语义标签后，算法往往将标签转化为两两相似度，这导致了以下问题：（1）构造两相似度矩阵需要巨大的存储空间和大量的计算，使得这些方法无法扩展到大规模数据集;（2）将标签转化为两相似度会丢失训练数据的类别信息。因此，这些方法不能使散列码保留标签所反映的区别性信息，因此，这些方法的检索精度受到影响。为了解决这些挑战，本文介绍了一种简单而有效的监督多模式哈希方法，称为标签一致矩阵分解哈希（LCMFH），其重点是直接利用语义标签来指导哈希学习过程。考虑到来自不同模态的相关数据具有语义相关性，LCMFH将异构数据转换为潜在语义空间，其中来自同一类别的多模态数据共享相同的表示。因此，由所获得的表示量化的散列码与原始数据的语义标签一致，并且因此对于跨模态相似性搜索任务可以具有更大的区分能力。在标准数据库上的实验表明，该算法的性能优于几个国家的最先进的方法。
Multimodal hashing has attracted much interest for cross-modal similarity search on large-scale multimedia data sets because of its efficiency and effectiveness. Recently, supervised multimodal hashing, which tries to preserve the semantic information obtained from the labels of training data, has received considerable attention for its higher search accuracy compared with unsupervised multimodal hashing. Although these algorithms are promising, they are mainly designed to preserve pairwise similarities. When semantic labels of training data are given, the algorithms often transform the labels into pairwise similarities, which gives rise to the following problems: (1) constructing pairwise similarity matrix requires enormous storage space and a large amount of calculation, making these methods unscalable to large-scale data sets; (2) transforming labels into pairwise similarities loses the category information of the training data. Therefore, these methods do not enable the hash codes to preserve the discriminative information reflected by labels and, hence, the retrieval accuracies of these methods are affected. To address these challenges, this paper introduces a simple yet effective supervised multimodal hashing method, called label consistent matrix factorization hashing (LCMFH), which focuses on directly utilizing semantic labels to guide the hashing learning procedure. Considering that relevant data from different modalities have semantic correlations, LCMFH transforms heterogeneous data into latent semantic spaces in which multimodal data from the same category share the same representation. Therefore, hash codes quantified by the obtained representations are consistent with the semantic labels of the original data and, thus, can have more discriminative power for cross-modal similarity search tasks. Thorough experiments on standard databases show that the proposed algorithm outperforms several state-of-the-art methods.
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影响因子：
6.5
作者：
Erkun Yang;Cheng Deng;W. Liu;Xianglong Liu;D. Tao;Xinbo Gao
通讯作者：
Erkun Yang;Cheng Deng;W. Liu;Xianglong Liu;D. Tao;Xinbo Gao
DOI：
--
发表时间：
2009-12
期刊：
--
影响因子：
--
作者：
M. Raginsky;Svetlana Lazebnik
通讯作者：
M. Raginsky;Svetlana Lazebnik
DOI：
10.1109/tpami.2016.2645565
发表时间：
2018-05
影响因子：
23.6
作者：
Feng Zheng;Yi Tang;Ling Shao
通讯作者：
Feng Zheng;Yi Tang;Ling Shao
DOI：
10.1109/tpami.2011.217
发表时间：
2012-07-01
影响因子：
23.6
作者：
Liu, Haifeng;Wu, Zhaohui;Huang, Thomas S.
通讯作者：
Huang, Thomas S.
用于最近邻搜索的查询自适应倒数哈希表
影响因子：
10.6
作者：
Liu, Xianglong;Deng, Cheng;Li, Xuelong
通讯作者：
Li, Xuelong