Cross-Modality Submodular Dictionary Learning for Information Retrieval

Zhu, Fan; Shao, Ling; Yu, Mengyang

doi:10.1145/2661829.2661926

Cited by 52 publications

(16 citation statements)

References 46 publications

(44 reference statements)

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“…In recent years, many improved algorithms have been proposed based on the CCA. Zhu et al [21] proposed a dictionary construction method for cross-media retrieval. Yao et al [22] proposed the Ranking Canonical Correlation Analysis method (RCCA).…”

Section: Related Workmentioning

confidence: 99%

Semi-Supervised Learning Based Semantic Cross-Media Retrieval

Zheng

Zhu

et al. 2021

IEEE Access

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With the advent of the era of big data, information has gradually changed from a single modal to a diversified form, such as image, text, video, audio, etc. With the growth of multimedia data, the key problem faced by cross-media retrieval technology is how to quickly retrieve multimedia data with different modalities of the same semantic. At present, many cross-media retrieval techniques use local annotated samples for training. In this way, the semantic information of the data cannot be fully utilized, and manual annotation is required, which is rather labor-intensive prone to errors and subjective viewing. Therefore, this paper proposes a Semi-Supervised learning based Semantic Cross-Media Retrieval (S3CMR) method aiming at the above problems. The main advantage of this method is to make full use of the relationship between the semantic information of the labeled samples and the unlabeled samples. Simultaneously, we integrate the linear regression term, correlation analysis term, and feature selection term into a joint cross-media learning framework. These terms interact with each other and embed more semantics in the shared subspace. Furthermore, an iterative method guaranteed with convergence is proposed to solve the formulated optimization problem. Experimental results on three publicly available datasets demonstrate that the proposed method outperforms eight state-of-the-art cross-media retrieval methods.

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Section: Related Workmentioning

confidence: 99%

Semi-Supervised Learning Based Semantic Cross-Media Retrieval

Zheng

Zhu

et al. 2021

IEEE Access

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“…Similar to classical discriminant analysis methods, in [3], two pairwise sets (must-link and cannot-link) on the crossmodal samples are considered to learn a similarity function. More references can be found [12], [17], [18], [19], [20], [21], [22].…”

Section: Related Workmentioning

confidence: 99%

“…Lai et al [38] proposed deep neural networks for simultaneous feature learning and hash functions learning. Zhu et al [18] proposed a cross-modal dictionary learning framework for representing multi-modal features with common sparse codes. Pereira et al [17] paid more attention on the role of semantic correlation matching in multi-modal retrieval.…”

Section: Related Workmentioning

confidence: 99%

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Hetero-Manifold Regularisation for Cross-Modal Hashing

Zheng

Tang²,

Shao

2018

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Recently, cross-modal search has attracted considerable attention but remains a very challenging task because of the integration complexity and heterogeneity of the multi-modal data. To address both challenges, in this paper, we propose a novel method termed hetero-manifold regularisation (HMR) to supervise the learning of hash functions for efficient cross-modal search. A hetero-manifold integrates multiple sub-manifolds defined by homogeneous data with the help of cross-modal supervision information. Taking advantages of the hetero-manifold, the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero-manifold. Furthermore, a novel cumulative distance inequality defined on the hetero-manifold is introduced to avoid the computational difficulty induced by the discreteness of hash codes. By using the inequality, cross-modal hashing is transformed into a problem of hetero-manifold regularised support vector learning. Therefore, the performance of cross-modal search can be significantly improved by seamlessly combining the integrated information of the hetero-manifold and the strong generalisation of the support vector machine. Comprehensive experiments show that the proposed HMR achieve advantageous results over the state-of-the-art methods in several challenging cross-modal tasks.

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“…Recently, some multi-view based sparse representation or DL methods have been presented [18][19][20][21][22][23]. Sparse multimodal biometrics recognition (SMBR) method [24] uses original training sample set as dictionary and exploits the joint sparsity of coding coefficients from different biometric modalities to make a joint decision.…”

Section: Introductionmentioning

confidence: 99%