參考文獻 |
[1] H. R. Sheikh and A. C. Bovik, "Image information and visual quality," IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 430-444, 2006.
[2] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.
[3] L. Kang, P. Ye, Y. Li, and D. Doermann, "Convolutional Neural Networks for No-Reference Image Quality Assessment," in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
[4] S. Bosse, D. Maniry, T. Wiegand, and W. Samek, "A deep neural network for image quality assessment," in 2016 IEEE International Conference on Image Processing (ICIP), 2016.
[5] S. Bianco, L. Celona, P. Napoletano, and R. Schettini, "On the Use of Deep Learning for Blind Image Quality Assessment," in Signal, Image And Video Processing, 2016.
[6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional," in NIPS, 2012.
[7] X. Lu, Z. Lin, H. Jin, J. Yang, and J. Z. Wang, "Rating Image Aesthetics Using Deep Learning," IEEE Transactions on Multimedia, vol. 17, no. 11, pp. 2021-2034, 2015.
[8] B. Jin, M. V. Ortiz Segovia, and S. Süsstrunk, "Image aesthetic predictors based on weighted CNNs," in 2016 IEEE International Conference on Image Processing (ICIP), 2016.
[9] N. Murray, L. Marchesotti, and F. Perronnin, "AVA: A large-scale database for aesthetic visual analysis," in 2012 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2012.
[10] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in arXiv:1409.1556, 2014.
[11] H. Talebi and P. Milanfar, "NIMA: Neural Image Assessment," IEEE Transactions on Image Processing, vol. 27, no. 8, pp. 3998-4011, 2018.
[12] N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, and C.-C. J. Kuo, "Color image database TID2013: Peculiarities and preliminary results," in European Workshop on Visual Information Processing (EUVIP), 2013.
[13] D. Ghadiyaram and A. C. Bovik, "Massive Online Crowdsourced Study of Subjective and Objective Picture Quality," IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 372-387, 2016.
[14] L. Hou, C.-P. Yu, and D. Samaras, "Squared Earth Mover′s Distance-based Loss for Training Deep Neural Networks," in arXiv:1611.05916, 2016.
[15] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," in arXiv:1704.04861, 2017.
[16] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in arXiv:1409.1556, 2014.
[17] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," in arXiv:1512.00567, 2015.
[18] H. Tong, M. Li, H.-J. Zhang, J. He, and C. Zhang, "Classification of Digital Photos Taken by Photographers or Home Users," Advances in Multimedia Information Processing - PCM 2004, pp. 198-205, 2004.
[19] R. Datta, D. Joshi, J. Li, and J. Z. Wang, "Studying Aesthetics in Photographic Images Using a Computational Approach," Computer Vision – ECCV 2006, pp. 288-301, 2006.
[20] L. Liu, R. Chen, L. Wolf, and D. Cohen-Or, "Optimizing Photo Composition," Computer Graphics Forum. Wiley Online Library, vol. 29, pp. 469-478, 2010.
[21] L. Marchesotti, F. Perronnin, D. Larlus, and G. Csurka, "Assessing the aesthetic quality of photographs using generic image descriptors," in 2011 International Conference on Computer Vision, 2011.
[22] M.-T. Wu, T.-Y. Pan, W.-L. Tsai, H.-C. Kuo, and M.-C. Hu, "High-level semantic photographic composition analysis and understanding with deep neural networks," in 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2017.
[23] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
[24] Y. Chen and T. Pock, "Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration," in IEEE transactions on pattern analysis and machine intelligence, 2017.
[25] L. Xu, J. S. Ren, C. Liu, and J. Jia, "Deep Convolutional Neural Network for Image Deconvolution," Advances in Neural Information Processing Systems, pp. 1790-1798, 2014.
[26] T. Acharya and A. K. Ray, Image Processing: Principles and Applications, 2005.
[27] H. Talebi and P. Milanfar, "Fast Multi-Layer Laplacian Enhancement," in arXiv:1606.07396, 2016.
[28] L. Shen, Z. Yue, F. Feng, Q. Chen, S. Liu, and J. Ma, "MSR-net:Low-light Image Enhancement Using Deep Convolutional Network," in arXiv:1711.02488, 2017.
[29] A. Ignatov, N. Kobyshev, R. Timofte, K. Vanhoey, and L. V. Gool, "DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks," in arXiv:1704.02470, 2017.
[30] H. Talebi and P. Milanfar, "Learned Perceptual Image Enhancement," in arXiv:1712.02864, 2017.
[31] V. Bychkovsky, S. Paris, E. Chan, and F. Durand, "Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs," in The Twenty-Fourth IEEE Conference on Computer Vision and Pattern Recognition, 2011.
[32] S. A. Esmaeili, B. Singh, and L. S. Davis, "Fast-At: Fast Automatic Thumbnail Generation Using Deep Neural Networks," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[33] J. Yan, S. Lin, S. B. Kang, and X. Tang, "Learning the Change for Automatic Image Cropping," in 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
[34] E. Hong, J. Jeon, and S. Lee, "CNN based Repeated Cropping for Photo Composition Enhancement," in CVPR workshop, 2017.
[35] Z. Wei, J. Zhang, X. Shen, Z. Lin, R. Mech, M. Hoai, and D. Samaras, "Good View Hunting: Learning Photo Composition from Dense View Pairs," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[36] X. Tang, W. Luo, and X. Wang, "Content-Based Photo Quality Assessment," in 2011 International Conference on Computer Vision, 2011.
[37] S. Ma, Z. Wei, F. Tian, X. Fan, J. Zhang, X. Shen, Z. Lin, J. Huang, R. Měch, D. Samaras, and H. Wang, "SmartEye: Assisting Instant Photo Taking via Integrating User Preference with Deep View Proposal Network," in 2019 CHI Conference on Human Factors in Computing Systems, 2019.
[38] D. Kim, T. Kwon, B. Yoo, G. Lee, W. Lee, J. Lee, S. Yim, and J. Jeong, "Seamless Capturing of Moments Using Photographic Compositions and Image Aesthetics," in 2020 International Conference on Electronics, Information, and Communication (ICEIC), 2020.
[39] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.
[40] J.-T. Lee, H.-U. Kim, C. Lee, and C.-S. Kim, "Photographic composition classification and dominant geometric element detection for outdoor scenes," Journal of Visual Communication and Image Representation, vol. 55, pp. 91-105, 2018.
[41] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, "PyTorch: An Imperative Style, High-Performance Deep Learning Library," in arXiv:1912.01703, 2019.
[42] K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask R-CNN," in IEEE International Conference on Computer Vision , 2017.
[43] T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, "Microsoft COCO: Common Objects in Context," in arXiv:1405.0312, 2014.
[44] M. Francisco and G. Ross, "maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch," 2018. [Online]. Available: https://github.com/facebookresearch/maskrcnn-benchmark.
[45] K. Man, K. Tang, and S. Kwong, "Genetic algorithms: concepts and applications [in engineering design]," IEEE Transactions on Industrial Electronics, vol. 43, no. 5, pp. 519-534, 1996.
[46] "OpenCV," [Online]. Available: https://opencv.org/. [Accessed 6 - Jun - 2018].
[47] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in arXiv:1512.03385, 2015.
[48] G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in arXiv:1608.06993, 2016.
[49] S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, "Aggregated Residual Transformations for Deep Neural Networks," in arXiv:1611.05431, 2016.
[50] N. Otsu, "A Tlreshold Selection Method," IEEE TRANSACTIONS ON SYSTREMS, MAN, AND CYBERNETICS, Vols. SMC-9, no. 1, pp. 62-66, 1979.
[51] J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vols. PAMI-8, no. 6, pp. 679-698, 1986. |