A Cross-Media Retrieval Algorithm Based on Consistency Preserving of Collaborative Representation

Shang, Fei; Zhang, Huaxiang; Ji,; Sun, Erxin; Liu, Li; Zeng, Hui

doi:10.20965/jaciii.2018.p0280

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…MDCR is a modality-dependent cross-modal retrieval method, which assigns different projection matrices to different cross-modal retrieval tasks, and greatly improves the efficiency of cross-modal retrieval. [38] is an advanced cross-modal retrieval method for dictionary learning. It learns different dictionaries for images and texts, and obtains corresponding collaborative representations.…”

Section: ) Compared Methodsmentioning

confidence: 99%

Semantic Consistency Cross-Modal Retrieval With Semi-Supervised Graph Regularization

Zhang

2020

IEEE Access

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Most of the existing cross-modal retrieval methods make use of labeled data to learn projection matrices for different modal data. These methods usually learn the original semantic space to bridge the heterogeneous gap, ignoring the rich semantic information contained in unlabeled data. Accordingly, a semantic consistency cross-modal retrieval with semi-supervised graph regularization (SCCMR) algorithm is proposed, which integrates the prediction of labels and the optimization of projection matrices into a unified framework to ensure that the solution obtained is globally optimal. At the same time, the method uses graph embedding to consider the nearest neighbors in the potential subspace of paired images and texts as well as images and texts with the same semantics. l 21-norm constraint is applied to the projection matrices to select the discriminative features for different modal data. The results show that our method outperforms several advanced methods on four commonly used cross-modal retrieval datasets.

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Section: ) Compared Methodsmentioning

confidence: 99%

Semantic Consistency Cross-Modal Retrieval With Semi-Supervised Graph Regularization

Zhang

2020

IEEE Access

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“…New methods of dictionary learning are constantly emerging recently. A novel method was carried out by Shang et al [18]. This method projects heterogeneous data into an isomorphic subspace using the representation coefficients and it is effective in cross-media retrieval.…”

Section: Related Workmentioning

confidence: 99%

Bagging-based cross-media retrieval algorithm

Yin²,

Hong³

et al. 2022

Soft Comput

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It is very challenging to propose a strong learning algorithm with high prediction accuracy of cross-media retrieval, while finding a weak learning algorithm which is slightly higher than that of random prediction is very easy. Inspired by this idea, we propose an imaginative Bagging based cross-media retrieval algorithm (called BCMR) in this paper. First, we utilize bootstrap sampling to carry out random sampling of the original training set. The amount of the sample abstracted by bootstrap is set to be same as the original dataset. Second, 50 bootstrap replicates are used for training 50 weak classifiers independently. We take advantage of homogenous individual classifiers and integrate eight different baseline methods in our experiments. Finally, we generate the final strong classifier from the 50 weak classifiers by the integration strategy of sample voting. We use collective wisdom to eliminate bad decisions so that the generalization ability of the integrated model could be greatly enhanced. Extensive experiments performed on three datasets show that BCMR can effectively improve the accuracy of cross-media retrieval.

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“…Otherwise, rel k � 0; R k is the number of related items in the top k returns. To evaluate the performance of the proposed GRMD retrieval method, we compare GRMD with the canonical correlation analysis (CCA) [22], kernel canonical correlation analysis (KCCA) [19], semantic matching (SM) [22], semantic correlation matching (SCM) [22], three-view canonical correlation analysis (T-V CCA) [42], generalized multiview linear discriminant analysis (GMLDA) [29], generalized multiview canonical correlation analysis (GMMFA) [29], modalitydependent cross-media retrieval (MDCR) [40], joint feature selection and subspace learning (JFSSL) [43], joint latent subspace learning and regression (JLSLR) [44], generalized semisupervised structured subspace learning (GSSSL) [45], a cross-media retrieval algorithm based on the consistency of collaborative representation (CRCMR) [46], cross-media retrieval based on linear discriminant analysis (CRLDA) [47], and cross-modal online low-rank similarity (CMOLRS) function learning method [48]. e descriptions and characteristics of the above comparison methods used in the whole experiment are summarized in Table 2.…”

Section: Experimental Settingsmentioning

confidence: 99%

Modality-Dependent Cross-Modal Retrieval Based on Graph Regularization

Wang

Kong

et al. 2020

Mobile Information Systems

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Nowadays, the heterogeneity gap of different modalities is the key problem for cross-modal retrieval. In order to overcome heterogeneity gaps, potential correlations of different modalities need to be mined. At the same time, the semantic information of class labels is used to reduce the semantic gaps between different modalities data and realize the interdependence and interoperability of heterogeneous data. In order to fully exploit the potential correlation of different modalities, we propose a crossmodal retrieval framework based on graph regularization and modality dependence (GRMD). Firstly, considering the potential feature correlation and semantic correlation, different projection matrices are learned for different retrieval tasks, such as image query text (I2T) or text query image (T2I). Secondly, utilizing the internal structure of original feature space constructs an adjacent graph with semantic information constraints which can make different labels of heterogeneous data closer to the corresponding semantic information. e experimental results on three widely used datasets demonstrate the effectiveness of our method.

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A Cross-Media Retrieval Algorithm Based on Consistency Preserving of Collaborative Representation

Cited by 9 publications

References 29 publications

Semantic Consistency Cross-Modal Retrieval With Semi-Supervised Graph Regularization

Semantic Consistency Cross-Modal Retrieval With Semi-Supervised Graph Regularization

Bagging-based cross-media retrieval algorithm

Modality-Dependent Cross-Modal Retrieval Based on Graph Regularization

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