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Cluster-based Joint Matrix Factorization Hashing for Cross-Modal Retrieval

Dimitrios Rafailidis, Fabio Crestani
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
In this study, we propose a cross-modal hashing method by following a cluster-based joint matrix factorization strategy.  ...  We formulate a joint matrix factorization process with the constraint that pushes the documents' representations of the different modalities and the cross-modal cluster representations into a common consensus  ...  To capture the inter-modality and intra-modality similarities at a neighborhood-based level, in this study we introduce a cluster-based joint matrix factorization method for cross-modal hashing.  ... 
doi:10.1145/2911451.2914710 dblp:conf/sigir/RafailidisC16 fatcat:d6wa7bh5evdktbfmke3fdp4n6a

Unsupervised multi-graph cross-modal hashing for large-scale multimedia retrieval

Liang Xie, Lei Zhu, Guoqi Chen
2016 Multimedia tools and applications  
graph cross-modal hashing for large-scale multimedia retrieval. (2016). Multimedia Tools and Applications. 75, (15), 9185-9204. Research Collection School Of Information Systems.  ...  Abstract With the advance of internet and multimedia technologies, large-scale multi-modal representation techniques such as cross-modal hashing, are increasingly demanded for multimedia retrieval.  ...  Collective Matrix Factorization Hashing (CMFH) [8] learns unified hash codes by the matrix factorization of each modalities.  ... 
doi:10.1007/s11042-016-3432-0 fatcat:hn5pwu2l35bqtkjru52c7wfh5i

Unsupervised Deep Cross-modality Spectral Hashing [article]

Tuan Hoang and Thanh-Toan Do and Tam V. Nguyen and Ngai-Man Cheung
2020 arXiv   pre-print
This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval.  ...  In the first step, we propose a novel spectral embedding-based algorithm to simultaneously learn single-modality and binary cross-modality representations.  ...  Collective Matrix Factorization Hashing (CMFH) [20] aims to find consistent hash codes from different views by collective matrix factorization.  ... 
arXiv:2008.00223v3 fatcat:i2xbdck5gncinhai362j2xnmpu

Cross-Modal Retrieval for CPSS Data

Fangming Zhong, Guangze Wang, Zhikui Chen, Feng Xia, Geyong Min
2020 IEEE Access  
The hashing based methods have been widely studied in building bilateral semantic associations of binary codes for cross-model retrieval.  ...  How to deal with the cross-modal retrieval problem for heterogeneous CPSS data has drawn considerable interests recently.  ...  Rafailidis and Crestani [29] proposed cluster-based joint matrix factorization hashing (C-JMFH) to generate cross-modal cluster representations for instances, which are incorporated into a joint matrix  ... 
doi:10.1109/access.2020.2967594 fatcat:nr4ghjvhi5c3vpg2ml4eq7x3ga

Flexible Cross-Modal Hashing

Guoxian Yu, Xuanwu Liu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
2020 IEEE Transactions on Neural Networks and Learning Systems  
Hashing has been widely adopted for large-scale data retrieval in many domains due to its low storage cost and high retrieval speed.  ...  FlexCMH first introduces a clustering-based matching strategy to explore the structure of each cluster and, thus, to find the potential correspondence between clusters (and samples therein) across modalities  ...  Clustering-based cross-modal matching strategy Unlike single-modal hashing, the correspondence between samples is crucial for multi-modal data fusion and retrieval.  ... 
doi:10.1109/tnnls.2020.3027729 pmid:33052870 fatcat:g56qbjvomzeaxc4jyegrh2j3cu

Cluster-wise Unsupervised Hashing for Cross-Modal Similarity Search [article]

Lu Wang, Jie Yang
2019 arXiv   pre-print
Large-scale cross-modal hashing similarity retrieval has attracted more and more attention in modern search applications such as search engines and autopilot, showing great superiority in computation and  ...  However, current unsupervised cross-modal hashing methods still have some limitations: (1)many methods relax the discrete constraints to solve the optimization objective which may significantly degrade  ...  Ding et al. proposed collective matrix factorization hashing (CMFH) that performs collective matrix factorization to learn unified hash codes [10] .  ... 
arXiv:1911.07923v2 fatcat:ef3ypnljnffttovyfayj5gpuum

A Comprehensive Survey on Cross-modal Retrieval [article]

Kaiye Wang, Qiyue Yin, Wei Wang, Shu Wu, Liang Wang
2016 arXiv   pre-print
In this paper, we first review a number of representative methods for cross-modal retrieval and classify them into two main groups: 1) real-valued representation learning, and 2) binary representation  ...  In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of multimodal data. It takes one type of data as the query to retrieve relevant data of another type.  ...  It learns unified hash codes by collective matrix factorization with a latent factor model from different modalities of one instance.  ... 
arXiv:1607.06215v1 fatcat:jfbmmlvzrvcmtmzezogzuxvvqu

MOON: Multi-Hash Codes Joint Learning for Cross-Media Retrieval

Donglin Zhang, Xiao-Jun Wu, He-Feng Yin, Josef Kittler
2021 Pattern Recognition Letters  
To this end, we develop a novel Multiple hash cOdes jOint learNing method (MOON) for cross-media retrieval.  ...  In recent years, cross-media hashing technique has attracted increasing attention for its high computation efficiency and low storage cost.  ...  With all these merits, therefore, hashing techniques have gained much attention, with many hashing based methods proposed for advanced cross-modal retrieval.  ... 
doi:10.1016/j.patrec.2021.07.018 fatcat:rtoisvhcxnav3jgbnk6alwqk5m

MESH: A Flexible Manifold-Embedded Semantic Hashing for Cross-Modal Retrieval

Fangming Zhong, Guangze Wang, Zhikui Chen, Feng Xia
2020 IEEE Access  
Hashing based methods for cross-modal retrieval has been widely explored in recent years.  ...  To address these issues, in this article, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH).  ...  For example, Collective Matrix Factorization Hashing (CMFH) [17] is proposed based on matrix factorization to learn the unified hash codes for different modalities.  ... 
doi:10.1109/access.2020.3015528 fatcat:k42zalqde5afbk5hlw3sj736im

Task-adaptive Asymmetric Deep Cross-modal Hashing [article]

Tong Wang, Lei Zhu, Zhiyong Cheng, Jingjing Li, Huaxiang Zhang
2020 arXiv   pre-print
Because of its advantages on retrieval and storage efficiency, it is widely used for solving efficient cross-modal retrieval.  ...  However, existing researches equally handle the different tasks of cross-modal retrieval, and simply learn the same couple of hash functions in a symmetric way for them.  ...  DLFH proposes an efficient hash learning algorithm based on the discrete latent factor model to directly learn binary hash codes for cross-modal retrieval.  ... 
arXiv:2004.00197v1 fatcat:qfyeezrxunblhlvjhe4nsj3a6m

Generalized Semantic Preserving Hashing for N-Label Cross-Modal Retrieval

Devraj Mandal, Kunal N. Chaudhury, Soma Biswas
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Hashing based techniques provide an attractive solution to this problem when the data size is large.  ...  Different scenarios of cross-modal matching are possible, for example, data from the different modalities can be associated with a single label or multiple labels, and in addition may or may not have one-to-one  ...  Viresh Ranjan (Stony Brook University) for useful discussions during the progress of this work.  ... 
doi:10.1109/cvpr.2017.282 dblp:conf/cvpr/MandalCB17 fatcat:sdzvy565gjfi5pjgnyfcwhi2j4

Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion [article]

Yang Wang
2020 arXiv   pre-print
With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects.  ...  Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces.  ...  [20] proposed a triplet-based deep hashing (TDH) network for cross-modal retrieval, which captured relative semantic correlation of various modalities in the supervised manner via triplet labels.  ... 
arXiv:2006.08159v1 fatcat:g4467zmutndglmy35n3eyfwxku

Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

Qibin Zheng, Xiaoguang Ren, Yi Liu, Wei Qin
2020 Mathematical Problems in Engineering  
To reduce the demands for training data, we propose a cross-modal retrieval framework that utilizes both coupled and uncoupled samples.  ...  Moreover, under this framework, we implement a cross-modal retrieval method based on the consistency between the semantic structure of multiple modalities.  ...  [18] proposed a sparse multimodal hashing method for cross-modal retrieval. Song et al.  ... 
doi:10.1155/2020/2503137 fatcat:37cxcuimjbfa7mdibjmeduztwe

Deep semantic cross modal hashing based on graph similarity of modal-specific

Junzheng Li
2021 IEEE Access  
Supervised Matrix Factorization Hashing for Cross-Modal Retrieval (SMFH) [19] is a supervised cross modal hashing method based on collective matrix factorization, which considers both the label consistency  ...  A Scalable disCRete mATrix faCtorization Hashing (SCRATCH) [36] for Cross Modal Retrieval generates the hash codes by the latent representations which are learned by utilizing Collective Matrix Factorization  ... 
doi:10.1109/access.2021.3093357 fatcat:uyouxawgzbhzhlrsufj4iauiuy

Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing [article]

Tuan Hoang, Thanh-Toan Do, Tam V. Nguyen, Ngai-Man Cheung
2021 arXiv   pre-print
In this paper, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval.  ...  Hence, balancing between reducing the modality gap and losing modality-private information is important for the cross-modal retrieval tasks.  ...  for each modality [23] , [25] , [28] , [39] , [40] or to a joint similarity matrix for all modalities [29] ).  ... 
arXiv:2112.06489v1 fatcat:5gfpy7t5jnbvjoodg4mrs4awxi
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