A reliable image quality assessment metric : evaluation using camera impacts
- Kaur, Roopdeep, Karmakar, Gour, Xia, Feng
- Authors: Kaur, Roopdeep , Karmakar, Gour , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition and Image Analysis Vol. 32, no. 3 (2022), p. 551-560
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- Description: Abstract: Image analysis is being applied in many applications including industrial automation with the Industrial Internet of Things and machine vision. The images captured by cameras, especially from the outdoor environment are impacted by various parameters such as lens blur, dirty lens, and lens distortion (barrel distortion). There exist many approaches that assess the impact of camera parameters on the quality of the images. However, most of these techniques do not use important quality assessment metrics such as oriented FAST and rotated BRIEF, and structural content. None of these techniques objectively evaluate the impact of barrel distortion on the image quality using quality assessment metrics such as mean square error, peak signal-to-noise ratio, structural content, oriented FAST, and rotated BRIEF, and structural similarity index. In this paper, besides lens dirtiness and blurring, we also examine the impact of barrel distortion using various types of datasets having different levels of barrel distortion. Analysis shows none of the existing metrics produces quality values consistent with intuitively defined impact levels for lens blur, dirtiness, and barrel distortion. To address the loopholes of existing metrics and make the quality assessment metric more reliable, we propose a new image quality assessment metric that fuses the quality values obtained from different metrics using a decision fusion technique known as the Dempster–Shafer theory. Our proposed metric produces quality values that are more consistent and conform with the perceptually defined camera parameter impact levels. For all the above-mentioned camera impacts, our proposed metric exhibits 100% assessment reliability, which includes an enormous improvement over other metrics. © 2022, Pleiades Publishing, Ltd.
- Authors: Kaur, Roopdeep , Karmakar, Gour , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition and Image Analysis Vol. 32, no. 3 (2022), p. 551-560
- Full Text:
- Reviewed:
- Description: Abstract: Image analysis is being applied in many applications including industrial automation with the Industrial Internet of Things and machine vision. The images captured by cameras, especially from the outdoor environment are impacted by various parameters such as lens blur, dirty lens, and lens distortion (barrel distortion). There exist many approaches that assess the impact of camera parameters on the quality of the images. However, most of these techniques do not use important quality assessment metrics such as oriented FAST and rotated BRIEF, and structural content. None of these techniques objectively evaluate the impact of barrel distortion on the image quality using quality assessment metrics such as mean square error, peak signal-to-noise ratio, structural content, oriented FAST, and rotated BRIEF, and structural similarity index. In this paper, besides lens dirtiness and blurring, we also examine the impact of barrel distortion using various types of datasets having different levels of barrel distortion. Analysis shows none of the existing metrics produces quality values consistent with intuitively defined impact levels for lens blur, dirtiness, and barrel distortion. To address the loopholes of existing metrics and make the quality assessment metric more reliable, we propose a new image quality assessment metric that fuses the quality values obtained from different metrics using a decision fusion technique known as the Dempster–Shafer theory. Our proposed metric produces quality values that are more consistent and conform with the perceptually defined camera parameter impact levels. For all the above-mentioned camera impacts, our proposed metric exhibits 100% assessment reliability, which includes an enormous improvement over other metrics. © 2022, Pleiades Publishing, Ltd.
Relational structure-aware knowledge graph representation in complex space
- Sun, Ke, Yu, Shuo, Peng, Ciyuan, Wang, Yueru, Alfarraj, Osama, Tolba, Amr, Xia, Feng
- Authors: Sun, Ke , Yu, Shuo , Peng, Ciyuan , Wang, Yueru , Alfarraj, Osama , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 11 (2022), p.
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- Description: Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Sun, Ke , Yu, Shuo , Peng, Ciyuan , Wang, Yueru , Alfarraj, Osama , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 11 (2022), p.
- Full Text:
- Reviewed:
- Description: Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Robust graph neural networks via ensemble learning
- Lin, Qi, Yu, Shuo, Sun, Ke, Zhao, Wenhong, Alfarraj, Osama, Tolba, Amr, Xia, Feng
- Authors: Lin, Qi , Yu, Shuo , Sun, Ke , Zhao, Wenhong , Alfarraj, Osama , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 8 (2022), p.
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- Description: Graph neural networks (GNNs) have demonstrated a remarkable ability in the task of semi-supervised node classification. However, most existing GNNs suffer from the nonrobustness issues, which poses a great challenge for applying GNNs into sensitive scenarios. Some researchers concentrate on constructing an ensemble model to mitigate the nonrobustness issues. Nevertheless, these methods ignore the interaction among base models, leading to similar graph representations. Moreover, due to the deterministic propagation applied in most existing GNNs, each node highly relies on its neighbors, leaving the nodes to be sensitive to perturbations. Therefore, in this paper, we propose a novel framework of graph ensemble learning based on knowledge passing (called GEL) to address the above issues. In order to achieve interaction, we consider the predictions of prior models as knowledge to obtain more reliable predictions. Moreover, we design a multilayer DropNode propagation strategy to reduce each node’s dependence on particular neighbors. This strategy also empowers each node to aggregate information from diverse neighbors, alleviating oversmoothing issues. We conduct experiments on three benchmark datasets, including Cora, Citeseer, and Pubmed. GEL outperforms GCN by more than 5% in terms of accuracy across all three datasets and also performs better than other state-of-the-art baselines. Extensive experimental results also show that the GEL alleviates the nonrobustness and oversmoothing issues. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Lin, Qi , Yu, Shuo , Sun, Ke , Zhao, Wenhong , Alfarraj, Osama , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 8 (2022), p.
- Full Text:
- Reviewed:
- Description: Graph neural networks (GNNs) have demonstrated a remarkable ability in the task of semi-supervised node classification. However, most existing GNNs suffer from the nonrobustness issues, which poses a great challenge for applying GNNs into sensitive scenarios. Some researchers concentrate on constructing an ensemble model to mitigate the nonrobustness issues. Nevertheless, these methods ignore the interaction among base models, leading to similar graph representations. Moreover, due to the deterministic propagation applied in most existing GNNs, each node highly relies on its neighbors, leaving the nodes to be sensitive to perturbations. Therefore, in this paper, we propose a novel framework of graph ensemble learning based on knowledge passing (called GEL) to address the above issues. In order to achieve interaction, we consider the predictions of prior models as knowledge to obtain more reliable predictions. Moreover, we design a multilayer DropNode propagation strategy to reduce each node’s dependence on particular neighbors. This strategy also empowers each node to aggregate information from diverse neighbors, alleviating oversmoothing issues. We conduct experiments on three benchmark datasets, including Cora, Citeseer, and Pubmed. GEL outperforms GCN by more than 5% in terms of accuracy across all three datasets and also performs better than other state-of-the-art baselines. Extensive experimental results also show that the GEL alleviates the nonrobustness and oversmoothing issues. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Subgraph adaptive structure-aware graph contrastive learning
- Chen, Zhikui, Peng, Yin, Yu, Shuo, Cao, Chen, Xia, Feng
- Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
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- Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
- Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
- Full Text:
- Reviewed:
- Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
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