1. NCCU Academic Hub
  2. 學術產出
  3. College of Informatics
  4. Theses
  5. Item

學術產出-Theses

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

題名 水下顯著物目標檢測
Underwater Salient Object Detection
作者 林祐丞
Lin, Yu-Cheng
貢獻者 彭彥璁
Peng, Yan­-Tsung
林祐丞
Lin, Yu-Cheng
關鍵詞 顯著物偵測
資料擴增
深度學習
Underwater salient object detection
Data augmentation
Deep learning
日期 2021
上傳時間 1-Nov-2021 12:00:43 (UTC+8)
摘要 顯著物偵測(SOD)在深度學習架構下已達到相當先進的成果。然而既有的研究大部分都專注在陸上場景，水下場景的顯著物偵測仍有待發展。在這篇論文中，我們蒐集並標註一水下顯著物資料集，用以驗證我們提出的模型方法。本論文中提出二種方法提昇顯著物偵測準確度。第一，我們先嘗試利用了水下影像模糊特性，幫助深度網路學習顯著物偵測。首先，我們會從原圖計算生成模糊圖，並與原圖一起輸入模型抽取特徵並融合，藉以提昇顯著物偵測準確度。第二，我們提出基於模糊圖對原圖增益作調整的一種資料擴增的方法。實驗結果顯示在最新顯著物偵測模型上，使用這兩種方法，皆可有效提昇效能。而提出的資料擴增方法的成效，比第一種方法更為有效。
Salient object detection (SOD) has achieved state-of-the-art performance with the help of deep networks. However, most of the works focus on terrestrial scenes, and underwater scenes for SOD are still unexplored. In this work, we propose two practical approaches to boost the performance of underwater SOD. First, we utilize image blurriness to enable a more accurate SOD prediction. The blurriness map is calculated based on the input image, fed into the model with the input, and fused with the input image to produce the saliency map. Next, we propose a data augmentation method called FocusAugment for underwater SOD, which adjusts the image intensity based on the blurriness map. We can modify images by highlighting less blurred regions or enlarging the difference of pixels based on the blurriness maps. We test underwater SOD by the proposed dataset collected and annotated by ourselves for evaluation. The experimental results show that both of our approaches work; moreover, the presented FocusAugment works better than the blurriness-guided SOD model.
參考文獻 [1] D. V. Ruiz, B. A. Krinski, and E. Todt, “Ida: Improved data augmentation applied to salient object detection,” in 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2020.
[2] Y. Pang, X. Zhao, L. Zhang, and H. Lu, “Multi­scale interactive network for salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
[3] D. Zhang, D. Meng, L. Zhao, and J. Han, “Bridging saliency detection to weakly supervised object detection based on self­paced curriculum learning,” arXiv preprint arXiv:1703.01290, 2017.
[4] Z. Ren, S. Gao, L.­T. Chia, and I. W.­H. Tsang, “Region­based saliency detection and its application in object recognition,” IEEE Transactions on Circuits and Systems for Video Technology, 2013.
[5] S. P. Bharati, S. Nandi, Y. Wu, Y. Sui, and G. Wang, “Fast and robust object tracking with adaptive detection,” in 2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI). IEEE, 2016.
[6] H. Lee and D. Kim, “Salient region­based online object tracking,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. 30
[7] M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, “Paying more attention to saliency: Image captioning with saliency and context attention,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2018.
[8] H. Hadizadeh and I. V. Bajić, “Saliency­aware video compression,” IEEE Transactions on Image Processing, vol. 23, no. 1, pp. 19–33, 2013.
[9] Q.­G. Ji, Z.­D. Fang, Z.­H. Xie, and Z.­M. Lu, “Video abstraction based on the visual attention model and online clustering,” Signal Processing: Image Communication, vol. 28, no. 3, pp. 241–253, 2013.
[10] Y. Ban and K. Lee, “Re­enrichment learning: Metadata saliency for the evolutive personalization of a recommender system,” Applied Sciences, vol. 11, no. 4, p. 1733, 2021.
[11] J. Zhang, X. Yu, A. Li, P. Song, B. Liu, and Y. Dai, “Weakly­supervised salient object detection via scribble annotations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 12 546–12 555.
[12] P. Siva, C. Russell, T. Xiang, and L. Agapito, “Looking beyond the image: Unsupervised learning for object saliency and detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 3238–3245.
[13] L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with image­level supervision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 136–145.
[14] D.­P. Fan, T. Li, Z. Lin, G.­P. Ji, D. Zhang, M.­M. Cheng, H. Fu, and J. Shen, “Rethinking co­salient object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
[15] M. J. Islam, R. Wang, K. de Langis, and J. Sattar, “Svam: Saliency­guided visual attention modeling by autonomous underwater robots,” arXiv preprint arXiv:2011.06252, 2020.
[16] M. J. Islam, P. Luo, and J. Sattar, “Simultaneous enhancement and superresolution of underwater imagery for improved visual perception,” arXiv preprint arXiv:2002.01155, 2020.
[17] L. Zhang, B. He, Y. Song, and T. Yan, “Underwater image feature extraction and matching based on visual saliency detection,” in OCEANS 2016­Shanghai. IEEE, 2016.
[18] A. Maldonado­Ramírez and L. A. Torres­Méndez, “Robotic visual tracking of relevant cues in underwater environments with poor visibility conditions,” Journal of Sensors, 2016.
[19] Microsoft azure cognitive services computer vision. Accessed on Nov. 2020.[Online]. Available: https://azure.microsoft.com/en­us/services/cognitive­services/computer­vision/
[20] R. Zhao, W. Ouyang, H. Li, and X. Wang, “Saliency detection by multi­context deep learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1265–1274.
[21] G. Li and Y. Yu, “Visual saliency based on multiscale deep features,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015.
[22] X. Li, L. Zhao, L. Wei, M.­H. Yang, F. Wu, Y. Zhuang, H. Ling, and J. Wang, “Deep saliency: Multi­task deep neural network model for salient object detection,” IEEE transactions on image processing, vol. 25, no. 8, pp. 3919–3930, 2016.
[23] L. Wang, L. Wang, H. Lu, P. Zhang, and X. Ruan, “Saliency detection with recurrent fully convolutional networks,” in European conference on computer vision. Springer, 2016, pp. 825–841.
[24] D. A. Klein and S. Frintrop, “Center­surround divergence of feature statistics for salient object detection,” in 2011 International Conference on Computer Vision IEEE, 2011.
[25] M.­M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S.­M. Hu, “Global contrast based salient region detection,” IEEE transactions on pattern analysis and machine intelligence, 2014.
[26] Z. Wang, D. Xiang, S. Hou, and F. Wu, “Background­driven salient object detection,” IEEE transactions on multimedia, 2016.
[27] Ç. Aytekin, H. Possegger, T. Mauthner, S. Kiranyaz, H. Bischof, and M. Gabbouj, “Spatiotemporal saliency estimation by spectral foreground detection,” IEEE Transactions on Multimedia, 2017.
[28] H. Peng, B. Li, W. Xiong, W. Hu, and R. Ji, “Rgbd salient object detection: a benchmark and algorithms,” in European conference on computer vision. Springer, 2014, pp. 92–109.
[29] J. Han, H. Chen, N. Liu, C. Yan, and X. Li, “Cnns­based rgb­d saliency detection via cross­view transfer and multiview fusion,” IEEE transactions on cybernetics, vol. 48, no. 11, pp. 3171–3183, 2017.
[30] Y.­T. Peng, X. Zhao, and P. C. Cosman, “Single underwater image enhancement using depth estimation based on blurriness,” in 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015, pp. 4952–4956.
[31] D. V. Ruiz, B. A. Krinski, and E. Todt, “Anda: A novel data augmentation technique applied to salient object detection,” in 2019 19th International Conference on Advanced Robotics (ICAR). IEEE, 2019.
[32] Q. Hou, M.­M. Cheng, X. Hu, A. Borji, Z. Tu, and P. H. Torr, “Deeply supervised salient object detection with short connections,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3203–3212.
[33] G. Lee, Y.­W. Tai, and J. Kim, “Deep saliency with encoded low level distance map and high level features,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 660–668.
[34] N. Liu and J. Han, “Dhsnet: Deep hierarchical saliency network for salient object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 678–686.
[35] J. Wei, S. Wang, and Q. Huang, “F3net: Fusion, feedback and focus for salient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
[36] X. Qin, Z. Zhang, C. Huang, C. Gao, M. Dehghan, and M. Jagersand, “Basnet: Boundary­aware salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
[37] X. Zhao, Y. Pang, L. Zhang, H. Lu, and L. Zhang, “Suppress and balance: A simple gated network for salient object detection,” in European Conference on Computer Vision. Springer, 2020, pp. 35–51.
[38] H. Zhou, X. Xie, J.­H. Lai, Z. Chen, and L. Yang, “Interactive two­stream decoder for accurate and fast saliency detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
[39] Z. Wu, L. Su, and Q. Huang, “Cascaded partial decoder for fast and accurate salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 3907–3916.
[40] X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 510–519.
[41] N. Liu, N. Zhang, and J. Han, “Learning selective self­mutual attention for rgbd saliency detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 13 756–13 765.
[42] B. Jiang, Z. Zhou, X. Wang, J. Tang, and B. Luo, “Cmsalgan: Rgb­d salient object detection with cross­view generative adversarial networks,” IEEE Transactions on Multimedia, vol. 23, pp. 1343–1353, 2020.
[43] J.­X. Zhao, J.­J. Liu, D.­P. Fan, Y. Cao, J. Yang, and M.­M. Cheng, “Egnet: Edge guidance network for salient object detection,” in Proc. Int’l Conf. Computer Vision, 2019.
[44] W. Wang, S. Zhao, J. Shen, S. C. Hoi, and A. Borji, “Salient object detection with pyramid attention and salient edges,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 1448–1457.
[45] H. Feng, X. Yin, L. Xu, G. Lv, Q. Li, and L. Wang, “Underwater salient object detection jointly using improved spectral residual and fuzzy c­means,” Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 329–339, 2019.
[46] Z. Chen, H. Gao, Z. Zhang, H. Zhou, X. Wang, and Y. Tian, “Underwater salient object detection by combining 2d and 3d visual features,” Neurocomputing, vol. 391, pp. 249–259, 2020.
[47] B. Xu, H. Liang, R. Liang, and P. Chen, “Locate globally, segment locally: A progressive architecture with knowledge review network for salient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021.
[48] M. Ma, C. Xia, and J. Li, “Pyramidal feature shrinking for salient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021.
[49] X. Qin, Z. Zhang, C. Huang, M. Dehghan, O. R. Zaiane, and M. Jagersand, “U2­net: Going deeper with nested u­structure for salient object detection,” Pattern Recognition, 2020.
[50] Z. Chen, Q. Xu, R. Cong, and Q. Huang, “Global context­aware progressive aggregation network for salient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
[51] J. Wei, S. Wang, Z. Wu, C. Su, Q. Huang, and Q. Tian, “Label decoupling framework for salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
[52] S.­H. Gao, Y.­Q. Tan, M.­M. Cheng, C. Lu, Y. Chen, and S. Yan, “Highly efficient salient object detection with 100k parameters,” in European Conference on Computer Vision. Springer, 2020.
[53] S. Mohammadi, M. Noori, A. Bahri, S. G. Majelan, and M. Havaei, “Cagnet: Content­aware guidance for salient object detection,” Pattern Recognition, 2020.
[54] M. Feng, H. Lu, and E. Ding, “Attentive feedback network for boundary­aware salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
[55] J.­J. Liu, Q. Hou, M.­M. Cheng, J. Feng, and J. Jiang, “A simple pooling­based design for real­time salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
[56] E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation policies from data,” arXiv preprint arXiv:1805.09501, 2018.
[57] S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim, “Fast autoaugment,” arXiv preprint arXiv:1905.00397, 2019.
[58] E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, “Randaugment: Practical automated data augmentation with a reduced search space,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.
[59] L. Perez and J. Wang, “The effectiveness of data augmentation in image classification using deep learning,” arXiv preprint arXiv:1712.04621, 2017.
[60] J. Lemley, S. Bazrafkan, and P. Corcoran, “Smart augmentation learning an optimal data augmentation strategy,” Ieee Access, 2017.
[61] M. Frid­Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “Gan­based synthetic medical image augmentation for increased cnn performance in liver lesion classification,” Neurocomputing, 2018.
[62] G. Mariani, F. Scheidegger, R. Istrate, C. Bekas, and C. Malossi, “Bagan: Data augmentation with balancing gan,” arXiv preprint arXiv:1803.09655, 2018.
[63] Hawaii udersea research laboratory. Accessed on Jun. 2019. [Online]. Available: http://www.soest.hawaii.edu/HURL/galleries.php
[64] Bubble vision. Accessed on Jun. 2019. [Online]. Available: https://www.bubblevision.com/
[65] National geographic. Accessed on Jun. 2019. [Online]. Available: https:// nationalgeographic.com/
[66] A. Bréhéret, “Pixel annotation tool,” https://github.com/abreheret/PixelAnnotationTool, 2017.
[67] J. Shi, Q. Yan, L. Xu, and J. Jia, “Hierarchical image saliency detection on extended cssd,” IEEE transactions on pattern analysis and machine intelligence, 2015.
[68] C. Yang, L. Zhang, H. Lu, X. Ruan, and M.­H. Yang, “Saliency detection via graphbased manifold ranking,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013.
[69] Y. Li, X. Hou, C. Koch, J. M. Rehg, and A. L. Yuille, “The secrets of salient object segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014.
[70] L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with image­level supervision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017.
[71] X. Xiao, Y. Zhou, and Y.­J. Gong, “Rgb­‘d’saliency detection with pseudo depth,” IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2126–2139, 2018.
[72] A. Galdran, D. Pardo, A. Picón, and A. Alvarez­Gila, “Automatic red­channel underwater image restoration,” Journal of Visual Communication and Image Representation, vol. 26, pp. 132–145, 2015.
[73] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequency­tuned salient region detection,” in Proc. Conf. Computer Vision and Pattern Recognition, 2009.
[74] R. Margolin, L. Zelnik­Manor, and A. Tal, “How to evaluate foreground maps?” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 248–255.
[75] F. Perazzi, P. Krähenbühl, Y. Pritch, and A. Hornung, “Saliency filters: Contrast based filtering for salient region detection,” in Proc. Conf. Computer Vision and Pattern Recognition, 2012.
[76] D. Fan, M. Cheng, Y. Liu, T. Li, and A. Borji, “Structure­measure: A new way to evaluate foreground maps,” in Proc. Int’l Conf. Computer Vision, 2017.
[77] D.­P. Fan, C. Gong, Y. Cao, B. Ren, M.­M. Cheng, and A. Borji, “Enhancedalignment measure for binary foreground map evaluation,” arXiv preprint arXiv:1805.10421, 2018.
描述 碩士
國立政治大學
資訊科學系
108753209
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108753209
資料類型 thesis
dc.contributor.advisor 彭彥璁zh_TW
dc.contributor.advisor Peng, Yan­-Tsungen_US
dc.contributor.author (Authors) 林祐丞zh_TW
dc.contributor.author (Authors) Lin, Yu-Chengen_US
dc.creator (作者) 林祐丞zh_TW
dc.creator (作者) Lin, Yu-Chengen_US
dc.date (日期) 2021en_US
dc.date.accessioned 1-Nov-2021 12:00:43 (UTC+8)-
dc.date.available 1-Nov-2021 12:00:43 (UTC+8)-
dc.date.issued (上傳時間) 1-Nov-2021 12:00:43 (UTC+8)-
dc.identifier (Other Identifiers) G0108753209en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/137677-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系zh_TW
dc.description (描述) 108753209zh_TW
dc.description.abstract (摘要) 顯著物偵測(SOD)在深度學習架構下已達到相當先進的成果。然而既有的研究大部分都專注在陸上場景，水下場景的顯著物偵測仍有待發展。在這篇論文中，我們蒐集並標註一水下顯著物資料集，用以驗證我們提出的模型方法。本論文中提出二種方法提昇顯著物偵測準確度。第一，我們先嘗試利用了水下影像模糊特性，幫助深度網路學習顯著物偵測。首先，我們會從原圖計算生成模糊圖，並與原圖一起輸入模型抽取特徵並融合，藉以提昇顯著物偵測準確度。第二，我們提出基於模糊圖對原圖增益作調整的一種資料擴增的方法。實驗結果顯示在最新顯著物偵測模型上，使用這兩種方法，皆可有效提昇效能。而提出的資料擴增方法的成效，比第一種方法更為有效。zh_TW
dc.description.abstract (摘要) Salient object detection (SOD) has achieved state-of-the-art performance with the help of deep networks. However, most of the works focus on terrestrial scenes, and underwater scenes for SOD are still unexplored. In this work, we propose two practical approaches to boost the performance of underwater SOD. First, we utilize image blurriness to enable a more accurate SOD prediction. The blurriness map is calculated based on the input image, fed into the model with the input, and fused with the input image to produce the saliency map. Next, we propose a data augmentation method called FocusAugment for underwater SOD, which adjusts the image intensity based on the blurriness map. We can modify images by highlighting less blurred regions or enlarging the difference of pixels based on the blurriness maps. We test underwater SOD by the proposed dataset collected and annotated by ourselves for evaluation. The experimental results show that both of our approaches work; moreover, the presented FocusAugment works better than the blurriness-guided SOD model.en_US
dc.description.tableofcontents 摘要 i
Abstract ii
Contents iii
List of Figures v
List of Tables vi
1 INTRODUCTION 1
2 RELATED WORKS 6
2.1 Deep SOD models 6
2.2 Data augmentation for SOD 8
3 PROPOSED DATASET 10
3.1 Dataset for underwater SOD 10
3.2 Dataset analysis and comparison 10
4 PROPOSED METHOD 14
4.1 Blurriness­guided SOD 14
4.1.1 Blurriness map generation 14
4.1.2 Blurriness­guided SOD model 16
4.2 Blurriness­guided augmentation for underwater SOD 17
5 EXPERIMENTS 20
5.1 Implementation and Experimental Setup 20
5.2 Evaluation Metrics 20
5.2.1 Precision­Recall (PR) curve 20
5.2.2 F­measure family 21
5.2.3 Mean absolute error (MAE) 21
5.2.4 S­measure 21
5.2.5 E­measure 22
5.3 Experimental results 22
5.3.1 Blurriness­guided underwater SOD 22
5.3.2 FocusAugment 22
5.4 Ablation sutdy 25
5.4.1 Blurriness­guided underwater SOD 25
5.4.2 FocusAugment 25
6 Conclusion 29
Reference 30		zh_TW
dc.format.extent 4341676 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108753209en_US
dc.subject (關鍵詞) 顯著物偵測zh_TW
dc.subject (關鍵詞) 資料擴增zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) Underwater salient object detectionen_US
dc.subject (關鍵詞) Data augmentationen_US
dc.subject (關鍵詞) Deep learningen_US
dc.title (題名) 水下顯著物目標檢測zh_TW
dc.title (題名) Underwater Salient Object Detectionen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] D. V. Ruiz, B. A. Krinski, and E. Todt, “Ida: Improved data augmentation applied to salient object detection,” in 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2020.
[2] Y. Pang, X. Zhao, L. Zhang, and H. Lu, “Multi­scale interactive network for salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
[3] D. Zhang, D. Meng, L. Zhao, and J. Han, “Bridging saliency detection to weakly supervised object detection based on self­paced curriculum learning,” arXiv preprint arXiv:1703.01290, 2017.
[4] Z. Ren, S. Gao, L.­T. Chia, and I. W.­H. Tsang, “Region­based saliency detection and its application in object recognition,” IEEE Transactions on Circuits and Systems for Video Technology, 2013.
[5] S. P. Bharati, S. Nandi, Y. Wu, Y. Sui, and G. Wang, “Fast and robust object tracking with adaptive detection,” in 2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI). IEEE, 2016.
[6] H. Lee and D. Kim, “Salient region­based online object tracking,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. 30
[7] M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, “Paying more attention to saliency: Image captioning with saliency and context attention,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2018.
[8] H. Hadizadeh and I. V. Bajić, “Saliency­aware video compression,” IEEE Transactions on Image Processing, vol. 23, no. 1, pp. 19–33, 2013.
[9] Q.­G. Ji, Z.­D. Fang, Z.­H. Xie, and Z.­M. Lu, “Video abstraction based on the visual attention model and online clustering,” Signal Processing: Image Communication, vol. 28, no. 3, pp. 241–253, 2013.
[10] Y. Ban and K. Lee, “Re­enrichment learning: Metadata saliency for the evolutive personalization of a recommender system,” Applied Sciences, vol. 11, no. 4, p. 1733, 2021.
[11] J. Zhang, X. Yu, A. Li, P. Song, B. Liu, and Y. Dai, “Weakly­supervised salient object detection via scribble annotations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 12 546–12 555.
[12] P. Siva, C. Russell, T. Xiang, and L. Agapito, “Looking beyond the image: Unsupervised learning for object saliency and detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 3238–3245.
[13] L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with image­level supervision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 136–145.
[14] D.­P. Fan, T. Li, Z. Lin, G.­P. Ji, D. Zhang, M.­M. Cheng, H. Fu, and J. Shen, “Rethinking co­salient object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
[15] M. J. Islam, R. Wang, K. de Langis, and J. Sattar, “Svam: Saliency­guided visual attention modeling by autonomous underwater robots,” arXiv preprint arXiv:2011.06252, 2020.
[16] M. J. Islam, P. Luo, and J. Sattar, “Simultaneous enhancement and superresolution of underwater imagery for improved visual perception,” arXiv preprint arXiv:2002.01155, 2020.
[17] L. Zhang, B. He, Y. Song, and T. Yan, “Underwater image feature extraction and matching based on visual saliency detection,” in OCEANS 2016­Shanghai. IEEE, 2016.
[18] A. Maldonado­Ramírez and L. A. Torres­Méndez, “Robotic visual tracking of relevant cues in underwater environments with poor visibility conditions,” Journal of Sensors, 2016.
[19] Microsoft azure cognitive services computer vision. Accessed on Nov. 2020.[Online]. Available: https://azure.microsoft.com/en­us/services/cognitive­services/computer­vision/
[20] R. Zhao, W. Ouyang, H. Li, and X. Wang, “Saliency detection by multi­context deep learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1265–1274.
[21] G. Li and Y. Yu, “Visual saliency based on multiscale deep features,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015.
[22] X. Li, L. Zhao, L. Wei, M.­H. Yang, F. Wu, Y. Zhuang, H. Ling, and J. Wang, “Deep saliency: Multi­task deep neural network model for salient object detection,” IEEE transactions on image processing, vol. 25, no. 8, pp. 3919–3930, 2016.
[23] L. Wang, L. Wang, H. Lu, P. Zhang, and X. Ruan, “Saliency detection with recurrent fully convolutional networks,” in European conference on computer vision. Springer, 2016, pp. 825–841.
[24] D. A. Klein and S. Frintrop, “Center­surround divergence of feature statistics for salient object detection,” in 2011 International Conference on Computer Vision IEEE, 2011.
[25] M.­M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S.­M. Hu, “Global contrast based salient region detection,” IEEE transactions on pattern analysis and machine intelligence, 2014.
[26] Z. Wang, D. Xiang, S. Hou, and F. Wu, “Background­driven salient object detection,” IEEE transactions on multimedia, 2016.
[27] Ç. Aytekin, H. Possegger, T. Mauthner, S. Kiranyaz, H. Bischof, and M. Gabbouj, “Spatiotemporal saliency estimation by spectral foreground detection,” IEEE Transactions on Multimedia, 2017.
[28] H. Peng, B. Li, W. Xiong, W. Hu, and R. Ji, “Rgbd salient object detection: a benchmark and algorithms,” in European conference on computer vision. Springer, 2014, pp. 92–109.
[29] J. Han, H. Chen, N. Liu, C. Yan, and X. Li, “Cnns­based rgb­d saliency detection via cross­view transfer and multiview fusion,” IEEE transactions on cybernetics, vol. 48, no. 11, pp. 3171–3183, 2017.
[30] Y.­T. Peng, X. Zhao, and P. C. Cosman, “Single underwater image enhancement using depth estimation based on blurriness,” in 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015, pp. 4952–4956.
[31] D. V. Ruiz, B. A. Krinski, and E. Todt, “Anda: A novel data augmentation technique applied to salient object detection,” in 2019 19th International Conference on Advanced Robotics (ICAR). IEEE, 2019.
[32] Q. Hou, M.­M. Cheng, X. Hu, A. Borji, Z. Tu, and P. H. Torr, “Deeply supervised salient object detection with short connections,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3203–3212.
[33] G. Lee, Y.­W. Tai, and J. Kim, “Deep saliency with encoded low level distance map and high level features,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 660–668.
[34] N. Liu and J. Han, “Dhsnet: Deep hierarchical saliency network for salient object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 678–686.
[35] J. Wei, S. Wang, and Q. Huang, “F3net: Fusion, feedback and focus for salient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
[36] X. Qin, Z. Zhang, C. Huang, C. Gao, M. Dehghan, and M. Jagersand, “Basnet: Boundary­aware salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
[37] X. Zhao, Y. Pang, L. Zhang, H. Lu, and L. Zhang, “Suppress and balance: A simple gated network for salient object detection,” in European Conference on Computer Vision. Springer, 2020, pp. 35–51.
[38] H. Zhou, X. Xie, J.­H. Lai, Z. Chen, and L. Yang, “Interactive two­stream decoder for accurate and fast saliency detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
[39] Z. Wu, L. Su, and Q. Huang, “Cascaded partial decoder for fast and accurate salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 3907–3916.
[40] X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 510–519.
[41] N. Liu, N. Zhang, and J. Han, “Learning selective self­mutual attention for rgbd saliency detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 13 756–13 765.
[42] B. Jiang, Z. Zhou, X. Wang, J. Tang, and B. Luo, “Cmsalgan: Rgb­d salient object detection with cross­view generative adversarial networks,” IEEE Transactions on Multimedia, vol. 23, pp. 1343–1353, 2020.
[43] J.­X. Zhao, J.­J. Liu, D.­P. Fan, Y. Cao, J. Yang, and M.­M. Cheng, “Egnet: Edge guidance network for salient object detection,” in Proc. Int’l Conf. Computer Vision, 2019.
[44] W. Wang, S. Zhao, J. Shen, S. C. Hoi, and A. Borji, “Salient object detection with pyramid attention and salient edges,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 1448–1457.
[45] H. Feng, X. Yin, L. Xu, G. Lv, Q. Li, and L. Wang, “Underwater salient object detection jointly using improved spectral residual and fuzzy c­means,” Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 329–339, 2019.
[46] Z. Chen, H. Gao, Z. Zhang, H. Zhou, X. Wang, and Y. Tian, “Underwater salient object detection by combining 2d and 3d visual features,” Neurocomputing, vol. 391, pp. 249–259, 2020.
[47] B. Xu, H. Liang, R. Liang, and P. Chen, “Locate globally, segment locally: A progressive architecture with knowledge review network for salient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021.
[48] M. Ma, C. Xia, and J. Li, “Pyramidal feature shrinking for salient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021.
[49] X. Qin, Z. Zhang, C. Huang, M. Dehghan, O. R. Zaiane, and M. Jagersand, “U2­net: Going deeper with nested u­structure for salient object detection,” Pattern Recognition, 2020.
[50] Z. Chen, Q. Xu, R. Cong, and Q. Huang, “Global context­aware progressive aggregation network for salient object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
[51] J. Wei, S. Wang, Z. Wu, C. Su, Q. Huang, and Q. Tian, “Label decoupling framework for salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
[52] S.­H. Gao, Y.­Q. Tan, M.­M. Cheng, C. Lu, Y. Chen, and S. Yan, “Highly efficient salient object detection with 100k parameters,” in European Conference on Computer Vision. Springer, 2020.
[53] S. Mohammadi, M. Noori, A. Bahri, S. G. Majelan, and M. Havaei, “Cagnet: Content­aware guidance for salient object detection,” Pattern Recognition, 2020.
[54] M. Feng, H. Lu, and E. Ding, “Attentive feedback network for boundary­aware salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
[55] J.­J. Liu, Q. Hou, M.­M. Cheng, J. Feng, and J. Jiang, “A simple pooling­based design for real­time salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
[56] E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation policies from data,” arXiv preprint arXiv:1805.09501, 2018.
[57] S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim, “Fast autoaugment,” arXiv preprint arXiv:1905.00397, 2019.
[58] E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, “Randaugment: Practical automated data augmentation with a reduced search space,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.
[59] L. Perez and J. Wang, “The effectiveness of data augmentation in image classification using deep learning,” arXiv preprint arXiv:1712.04621, 2017.
[60] J. Lemley, S. Bazrafkan, and P. Corcoran, “Smart augmentation learning an optimal data augmentation strategy,” Ieee Access, 2017.
[61] M. Frid­Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “Gan­based synthetic medical image augmentation for increased cnn performance in liver lesion classification,” Neurocomputing, 2018.
[62] G. Mariani, F. Scheidegger, R. Istrate, C. Bekas, and C. Malossi, “Bagan: Data augmentation with balancing gan,” arXiv preprint arXiv:1803.09655, 2018.
[63] Hawaii udersea research laboratory. Accessed on Jun. 2019. [Online]. Available: http://www.soest.hawaii.edu/HURL/galleries.php
[64] Bubble vision. Accessed on Jun. 2019. [Online]. Available: https://www.bubblevision.com/
[65] National geographic. Accessed on Jun. 2019. [Online]. Available: https:// nationalgeographic.com/
[66] A. Bréhéret, “Pixel annotation tool,” https://github.com/abreheret/PixelAnnotationTool, 2017.
[67] J. Shi, Q. Yan, L. Xu, and J. Jia, “Hierarchical image saliency detection on extended cssd,” IEEE transactions on pattern analysis and machine intelligence, 2015.
[68] C. Yang, L. Zhang, H. Lu, X. Ruan, and M.­H. Yang, “Saliency detection via graphbased manifold ranking,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013.
[69] Y. Li, X. Hou, C. Koch, J. M. Rehg, and A. L. Yuille, “The secrets of salient object segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014.
[70] L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with image­level supervision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017.
[71] X. Xiao, Y. Zhou, and Y.­J. Gong, “Rgb­‘d’saliency detection with pseudo depth,” IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2126–2139, 2018.
[72] A. Galdran, D. Pardo, A. Picón, and A. Alvarez­Gila, “Automatic red­channel underwater image restoration,” Journal of Visual Communication and Image Representation, vol. 26, pp. 132–145, 2015.
[73] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequency­tuned salient region detection,” in Proc. Conf. Computer Vision and Pattern Recognition, 2009.
[74] R. Margolin, L. Zelnik­Manor, and A. Tal, “How to evaluate foreground maps?” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 248–255.
[75] F. Perazzi, P. Krähenbühl, Y. Pritch, and A. Hornung, “Saliency filters: Contrast based filtering for salient region detection,” in Proc. Conf. Computer Vision and Pattern Recognition, 2012.
[76] D. Fan, M. Cheng, Y. Liu, T. Li, and A. Borji, “Structure­measure: A new way to evaluate foreground maps,” in Proc. Int’l Conf. Computer Vision, 2017.
[77] D.­P. Fan, C. Gong, Y. Cao, B. Ren, M.­M. Cheng, and A. Borji, “Enhancedalignment measure for binary foreground map evaluation,” arXiv preprint arXiv:1805.10421, 2018.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202101682en_US