Abstract
This paper will take the GIS system as a typical interface, and analyze the main design elements such as information structure, interface layout and element com-position. Based on the combination coding characteristics of cognitive complexity, a visual representation method is established. Through the preliminary mapping of visual complexity factors and physiological indicators, the mapping relation-ship between digital interface visual information and cognitive brain mechanism of information weapon system is proposed. Finally, the design strategy of GIS interface optimization complexity is proposed, which provides innovative ideas for the study of interface visual complexity.
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References
Berlyne, D.E.: Complexity and incongruity variables as determinants of exploratory choice and evaluative ratings. Can. J. Psychol. 17, 274–290 (1963)
Geissler, G.L., Watson, Z.R.T.: The influence of home page complexity on consumer attention, attitudes, and purchase intent. J. Advert. 35, 69–80 (2006)
Machado, P., Romero, J., Nadal, M., Santos, A., Correia, J., Carballal, A.: Computerized measures of visual complexity. Acta Physiol. 160, 43–57 (2015)
Oliva, A., Mack, M.L., Shrestha, M.: Identifying the perceptual dimensions of visual complexity of scenes. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 3, no. 49, pp. 1041–1046 (2004)
Stickel, C., Ebner, M., Holzinger, A.: The XAOS metric – understanding visual complexity as measure of usability. In: Leitner, G., Hitz, M., Holzinger, A. (eds.) HCI in Work and Learning, Life and Leisure, USAB 2010. Lecture Notes in Computer Science, vol. 6389, pp. 278–290. Springer, Heidelberg (2010)
Huo, J.: Image complexity and visual working memory capacity. In: Chen, L., Kapoor, S., Bhatia, R. (eds.) Emerging Trends and Advanced Technologies for Computational Intelligence. Studies in Computational Intelligence, vol. 647, pp. 301–314. Springer, Cham (2016)
Corchs, S.E., Ciocca, G., Bricolo, E., Gasparini, F.: Predicting complexity perception of real world images. PLoS ONE 11(6), 1–22 (2016)
Chen, Y.Q., Duan, J., Zhu, Y., Qian, X.F., Xiao, B.: Research on the image complexity based on neural network. In: International Conference on Machine Learning and Cybernetics, pp. 295–300. IEEE, Guangzhou (2015)
Silva, M.P.D., Courboulay, V., Estraillier, P.: Image complexity measure based on visual attention. In: Proceedings-International Conference on Image Processing, pp. 3281–3284. IEEE, Belgium (2011)
Bonev, B., Chuang, L.L., Escolano, F.: How do image complexity, task demands and looking biases influence human gaze behavior? Pattern Recogn. Lett. 34(7), 723–730 (2013)
Tseng, K.T., Tseng, Y.C.: The correlation between visual complexity and user trust in on-line shopping: implications for design. In: Kurosu, M. (eds.) Human-Computer Interaction. Applications and Services, HCI 2014. Lecture Notes in Computer Science, vol. 8512. Springer, Cham (2014)
Wang, Q., Yang, S., Cao, Z., Liu, M., Ma, Q.: An eye-tracking study of website complexity from cognitive load perspective. Decis. Support Syst. 62(1246), 1–10 (2014)
Rigau, J., Feixas, M., Sbert, M.: An information-theoretic framework for image complexity. In: Neumann, L., Sbert, M., Gooch, B., Purgathofer, W. (eds.) Computational Aesthetics in Graphics, Visualization and Imaging, pp. 177–184. Wiley-Blackwell, Hoboken (2006)
Mario, I., Chacon, M., Alma, D., Corral, S.: Image complexity measure: a human criterion free approach. In: Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, pp. 241–246. IEEE (2005)
Peters, R.A.I.: Image complexity metrics for automatic target recognizers. In: Proceedings of the Automatic Target Recognizer System and Technology Conference, pp. 1–17. Citeseer (1990)
Rusu, A., Govindaraju, V.: The influence of image complexity on handwriting recognition. In: Proceedings of the Tenth International Workshop on Frontiers in Handwriting Recognition (IWFHR 2006), La Baule, France (2006)
Li, M., Bai, M.: A mixed edge based text detection method by applying image complexity analysis. In: Proceedings of the 10th World Congress on Intelligent Control and Automation, pp. 4809–4814. IEEE Press, Beijing (2012)
Liu, Q., Sung, A.H., Ribeiro, B., Wei, M., Chen, Z., Xu, J.: Image complexity and feature mining for steganalysis of least significant bit matching steganography. Inf. Sci. 178(1), 21–36 (2008)
Carvajal-Gamez, B.E., Gallegos-Funes, F.J., Rosales-Silva, A.J.: Color local complexity estimation based steganographic (CLCES) method. Expert Syst. Appl. 40(4), 1132–1142 (2013)
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This paper is supported by the National Natural Science Foundation of China (No. 71871056, 71471037).
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Wang, S., Xue, C., Zhang, J., Shao, J. (2020). Interface Design of GIS System Based on Visual Complexity. In: Ahram, T., Falcão, C. (eds) Advances in Usability and User Experience. AHFE 2019. Advances in Intelligent Systems and Computing, vol 972. Springer, Cham. https://doi.org/10.1007/978-3-030-19135-1_70
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