An Automatic Contrast Validation Approach for Smartphone Themes
Resumo
The smartphone themes submitted to the themestore are first evaluated by human experts to ensure a pleasant visual experience1. One of the main challenges in smartphone themes evaluation is to validate the contrast of the elements of the theme. Contrast refers to the difference in visual properties that makes an object distinguishable from other objects and the background. In this work, we propose an automatic themes evaluation approach that validates the contrast of Android smartphone themes among regular and non-regular at the element level. To localize the contrast affected regions, the proposed themes evaluation is divided into two phases: 1) Element extraction; and 2) Contrast evaluation. For element extraction, a customized framework is created that utilizes native Android frameworks. Following, these elements are analyzed by using a specifically designed Convolutional Neural Network (CNN) for smartphone theme elements contrast evaluation. The proposed approach is evaluated on two databases which are composed of using the real Android themes. Experiments suggest that our proposed contrast-detection network can obtain a better performance than the known state-of-the-art classification methods, in smartphone themes evaluation, based on evaluation measures like Accuracy, F1 score, processing time and others.Referências
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E. Fernandes, R. Correia, A. Gil, J. Postal, and M. R. Gadelha, "Themes validation tool," in International Conference on Human-Computer Interaction. Springer, 2019, pp. 16–22.
N. Patil, D. Bhole, and P. Shete, "Enhanced ui automator viewer with improved android accessibility evaluation features," in 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE, 2016, pp. 977–983.
J. Kraunelis, Y. Chen, Z. Ling, X. Fu, and W. Zhao, "On malware leveraging the android accessibility framework," in International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services. Springer, 2013, pp. 512–523.
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C. Szegedy, Wei Liu, Yangqing 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, pp. 1–9.
A. Khurshid, S. C. and R. Grunitzki, "A scene classification approach for augmented reality devices," in International Conference on Human-Computer Interaction, vol. 46. Springer, 2020, p. LNCS 12428. [Online]. Available: https://www.researchgate.net/publication/343654512 A Scene Classification Approach for Augmented Reality Devices
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T. A. Nguyen and C. Csallner, "Reverse engineering mobile application user interfaces with remauit," in 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2015, pp. 248–259.
M. Mozgovoy and E. Pyshkin, "Using image recognition for testing handdrawn graphic user interfaces," in 11th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM 2017), 2017, pp. 12–16.
M. Abdullah-Al-Wadud, M. H. Kabir, M. A. A. Dewan, and O. Chae, "A dynamic histogram equalization for image contrast enhancement," IEEE Transactions on Consumer Electronics, vol. 53, no. 2, pp. 593–600, 2007.
T. Celik and T. Tjahjadi, "Automatic image equalization and contrast enhancement using gaussian mixture modeling," IEEE transactions on image processing, vol. 21, no. 1, pp. 145–156, 2011.
A. Zadorozny and H. Zhang, "Contrast enhancement using morphological scale space," in 2009 IEEE International Conference on Automation and Logistics. IEEE, 2009, pp. 804–807.
J. Tang, E. Peli, and S. Acton, "Image enhancement using a contrast measure in the compressed domain," IEEE signal processing LETTERS, vol. 10, no. 10, pp. 289–292, 2003.
S. Hashemi, S. Kiani, N. Noroozi, and M. E. Moghaddam, "An image contrast enhancement method based on genetic algorithm," Pattern Recognition Letters, vol. 31, no. 13, pp. 1816–1824, 2010.
Y. S. Moon, B. G. Han, H. S. Yang, and H. G. Lee, "Low Contrast Image Enhancement Using Convolutional Neural Network with Simple Reflection Model," Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 159–164, 2019.
M. Shah, S. Mishra, M. Sarkar, and C. Rout, "Identification of robust focus measure functions for the automated capturing of focused images from ziehl–neelsen stained sputum smear microscopy slide," Cytometry Part A, vol. 91, no. 8, pp. 800–809, 2017.
M. Cheng, N. J. Mitra, X. Huang, P. H. S. Torr, and S. Hu, "Global contrast based salient region detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 569–582, 2015.
Q. Yu, P. Zhang, J. Qiu, and F. Fang, "Perceptual learning of contrast detection in the human lateral geniculate nucleus," Current Biology, vol. 26, no. 23, pp. 3176–3182, 2016.
Y. M. Wang, "Performing spatial and temporal image contrast detection in pixel array," Jan. 26 2016, uS Patent 9,247,109.
E. Fernandes, R. Correia, A. Gil, J. Postal, and M. R. Gadelha, "Themes validation tool," in International Conference on Human-Computer Interaction. Springer, 2019, pp. 16–22.
N. Patil, D. Bhole, and P. Shete, "Enhanced ui automator viewer with improved android accessibility evaluation features," in 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE, 2016, pp. 977–983.
J. Kraunelis, Y. Chen, Z. Ling, X. Fu, and W. Zhao, "On malware leveraging the android accessibility framework," in International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services. Springer, 2013, pp. 512–523.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097–1105.
C. Szegedy, Wei Liu, Yangqing 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, pp. 1–9.
A. Khurshid, S. C. and R. Grunitzki, "A scene classification approach for augmented reality devices," in International Conference on Human-Computer Interaction, vol. 46. Springer, 2020, p. LNCS 12428. [Online]. Available: https://www.researchgate.net/publication/343654512 A Scene Classification Approach for Augmented Reality Devices
A. Khurshid, S. C. Tamayo, E. Fernandes, M. R. Gadelha, and M. Teofilo, "A robust and real-time face anti-spoofing method based on texture feature analysis," in International Conference on Human-Computer Interaction. Springer, 2019, pp. 484–496.
Publicado
07/11/2020
Como Citar
KHURSHID, Aasim; GIL, Adriano; GUIMARÃES, Felipe Augusto Souza; GADELHA, Mikhail; FERNANDES, Everlandio.
An Automatic Contrast Validation Approach for Smartphone Themes. In: WORKSHOP DE APLICAÇÕES INDUSTRIAIS - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 219-224.
DOI: https://doi.org/10.5753/sibgrapi.est.2020.13015.
