ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

Ranjbarzadeh, Ramin; Sadeghi, Soroush; Fadaeian, Aida; Ghoushchi, Saeid Jafarzadeh; Tirkolaee, Erfan Babaee; Caputo, Annalina; Bendechache, Malika

doi:10.1007/s00500-023-08983-3

Cited by 4 publications

(1 citation statement)

References 71 publications

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“…This paper utilizes deep learning technology to enhance the selection of emotional design through the separation of appearance and color. Deep learning technology can solve many problems, such as object detection, image classification, image segmentation (Ranjbarzadeh & Tirkolaee, 2023), and image generation, and has been widely applied in various industries (Weber, Arabnia, Aydın, Tirkolaee, 2023;Song, Li, et al, 2022;Wu, 2022;Morales & Suárez-Rocha, 2022;Dayyala, 2022). Deep learning (Miao & Ruomu, 2023;Zhou & Fang, 2022) is a machine learning subfield originating from the study of artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Emotion Generation Model Combining CGAN and pix2pix

Wang,

Ghani,

Zhou

2023

Journal of Organizational and End User Computing

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Computer vision has made significant advancements in emotional design. Designers can now utilize computer vision to create emotionally captivating designs that deeply resonate with people. This article aims at enhancing emotional design selection by separating appearance and color. A two-stage emotional design method is proposed, which yields significantly better results compared to classical single-stage methods.. In the Radboud face dataset (RaFD), facial expressions primarily rely on appearance, while color plays a relatively smaller role. Therefore, the two-stage model presented in this article can focus on shape design. By utilizing the SSIM image quality evaluation index, our model demonstrates a 31.63% improvement in generation performance compared to the CGAN model. Additionally, the PSNR image quality evaluation index shows a 10.78% enhancement in generation performance. The proposed model achieves superior design results and introduces various design elements.This article exhibits certain improvements in design effectiveness and scalability compared to conventional models.

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Section: Introductionmentioning

confidence: 99%

A Two-Stage Emotion Generation Model Combining CGAN and pix2pix

Wang,

Ghani,

Zhou

2023

Journal of Organizational and End User Computing

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Self-attention-based generative adversarial network optimized with color harmony algorithm for brain tumor classification

et al. 2024

Electromagnetic Biology and Medicine

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Cross-lingual Sentiment Analysis of Tamil Language Using a Multi-stage Deep Learning Architecture

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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In recent years, sentiment analysis has become a focal point in natural language processing. Cross-lingual sentiment analysis is a particularly demanding yet essential task that seeks to construct models capable of effectively analysing sentiments across a variety of languages. The primary motivation behind this research is to bridge the gap in current techniques which often struggle to perform well with low-resource languages, due to the scarcity of large, annotated datasets and their unique linguistic characteristics. In light of these challenges, we propose a novel Multi-Stage Deep Learning Architecture (MSDLA) for cross-lingual sentiment analysis of the Tamil language, a low-resource language. Our approach utilizes transfer learning from a source language with abundant resources to overcome data limitations. Our proposed model significantly outperforms existing methods on the Tamil Movie Review dataset, achieving an accuracy, precision, recall, and F1-score of 0.8772, 0.8614, 0.8825, and 0.8718 respectively. ANOVA statistical comparison demonstrates that the MSDLA’s improvements over other models, including mT5, XLM, mBERT, ULMFiT, BiLSTM, LSTM with Attention, and ALBERT with Hugging Face English Embedding are significant, with p-values all less than 0.005. Ablation studies confirm the importance of both cross-lingual semantic attention and domain adaptation in our architecture. Without these components, the model’s performance drops to 0.8342 and 0.8043 in accuracy, respectively. Furthermore, MSDLA demonstrates robust cross-domain performance on the Tamil News Classification and Thirukkural datasets, achieving an accuracy of 0.8551 and 0.8624 respectively, significantly outperforming the baseline models. These findings illustrate the robustness and efficacy of our approach, making a significant contribution to cross-lingual sentiment analysis techniques, especially for low-resource languages.

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ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

Cited by 4 publications

References 71 publications

A Two-Stage Emotion Generation Model Combining CGAN and pix2pix

A Two-Stage Emotion Generation Model Combining CGAN and pix2pix

Self-attention-based generative adversarial network optimized with color harmony algorithm for brain tumor classification

Cross-lingual Sentiment Analysis of Tamil Language Using a Multi-stage Deep Learning Architecture

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