Abstract
According to urban planner Kevin Lynch, imageability is the ability of a physical object to evoke a strong image in any viewer, making it memorable. The concept of imageability is important for architects and urban designers, so that their creations meet the needs of the citizens and improve the aesthetics of the place. Recently, computer vision and textual analysis techniques have been investigated for calculating the imageability of a place. In this paper, we propose a novel multi-modal system that utilises both visual and textual analysis methods to estimate the imageability score of a place. In addition, an image sentiment analysis deep learning model had been developed to provide supplementary information about the sentiment that is evoked to citizens by urban locations. Finally, a text generation algorithm is used to provide an explanation of the information extracted by the data analysis in a form of text to facilitate the works of architects and urban designers.
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Notes
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- 4.
In this work, we experimented with free-text comments that were apriori about spaces, as stated in Sect. 4. Therefore, we left other challenges such as the separation of texts about spaces and happenings in spaces for future research.
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The concept “rotonda” is taken from user describing nearby roundabout which is not captured on the image.
References
Paivio, A., Yuille, J.C., Madigan, S.A.: Concreteness, imagery, and meaningfulness values for 925 nouns. J. Exp. Psychol. 76, 1–25 (1968)
Lynch, K.: The Image of the City, vol. 11. MIT press, Cambridge (1960)
Ortis, A., Farinella, G., Battiato, S.: An overview on image sentiment analysis: methods. datasets and current challenges. ICETE, 290–300 (2019). https://doi.org/10.5220/0007909602900300
You, Q., Luo, J., Jin, H., Yang, J.: Robust image sentiment analysis using progressively trained and domain transferred deep networks. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 381–388. AAAI Press (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Zhou, B., Lapedriza, À., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1452–1464 (2017). https://doi.org/10.1109/TPAMI.2017.2723009
Rofes, A., et al.: Imageability ratings across languages. Behav. Res. Methods 50(3), 1187–1197 (2017). https://doi.org/10.3758/s13428-017-0936-0
Shvets, A., Wanner, L.: Concept extraction using pointer–generator networks and distant supervision for data augmentation. In: Keet, C.M., Dumontier, M. (eds.) EKAW 2020. LNCS (LNAI), vol. 12387, pp. 120–135. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61244-3_8
Ljubešić, N., Fišer, D., Peti-Stantić, A.: Predicting concreteness and imageability of words within and across languages via word embeddings. arXiv preprint arXiv:1807.02903 (2018)
Scott, G.G., Keitel, A., Becirspahic, M., Yao, B., Sereno, S.C.: The glasgow norms: ratings of 5,500 words on nine scales. Behav. Res. Methods 51(3), 1258–1270 (2018). https://doi.org/10.3758/s13428-018-1099-3
Umemura, K., et al.: Tell as you imagine: sentence imageability-aware image captioning. In: Lokoč, J., et al. (eds.) MMM 2021. LNCS, vol. 12573, pp. 62–73. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67835-7_6
Mille, S., Carlini, R., Burga, A., Wanner, L.: FORGe at SemEval-2017 Task 9: deep sentence generation based on a sequence of graph transducers. In: Proceedings of the 11th International Workshop on Semantic Evaluation, Vancouver, pp. 920–923 (2017)
Meenar, M., Afzalan, N., Hajrasouliha, A.: Analyzing Lynch’s city imageability in the digital age. J. Plan. Educ. Res., 0739456X19844573 (2019)
McCunn, L.J., Gifford, R.: Spatial navigation and place imageability in sense of place. Cities 74, 208–218 (2018)
LNCS Homepage. www.springer.com/lncs. Accessed 21 Nov 2016
Quercia, D., O’Hare, N.K., Cramer, H.: Aesthetic capital: what makes London look beautiful, quiet, and happy?. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 945–955 (2014)
Porzi, L., Rota Buló, S., Lepri, B., Ricci, E.: Predicting and understanding urban perception with convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 139–148 (2015)
Dubey, A., Naik, N., Parikh, D., Raskar, R., Hidalgo, C.A.: Deep learning the city: quantifying urban perception at a global scale. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 196–212. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_12
Qiu, W., Li, W., Liu, X., Huang, X.: Subjective street scene perceptions for Shanghai with large-scale application of computer vision and machine learning (No. 6166). EasyChair (2021)
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
Zhou, B., et al.: Semantic understanding of scenes through the ade20k dataset. Int. J. Comput. Vision 127(3), 302–321 (2019)
Biljecki, F., Ito, K.: Street view imagery in urban analytics and GIS: a review. Landscape Urban Plan. 215, 104217 (2021)
Isola, P., Xiao, J., Parikh, D., Torralba, A., Oliva, A.: What makes a photograph memorable? IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1469–1482 (2013)
Hasler, D., Suesstrunk, S.E.: Measuring colorfulness in natural images. In: Human Vision and Electronic Imaging VIII, vol. 5007, pp. 87–95. International Society for Optics and Photonics (2003)
Mel’čuk, I.: Dependency Syntax. State University of New York Press, Albany (1988)
Huang, J., Obracht-Prondzynska, H., Kamrowska-Zaluska, D., Sun, Y., Li, L.: The image of the city on social media: a comparative study using “Big Data’’ and “Small Data’’ methods in the Tri-City Region in Poland. Landscape Urban Plan. 206, 103977 (2021)
Kastner, M.A., et al.: Estimating the imageability of words by mining visual characteristics from crawled image data. Multimedia Tools Appl. 79(3), 18167–18199 (2020). https://doi.org/10.1007/s11042-019-08571-4
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). https://doi.org/10.1145/219717.219748
Manzo, L.C., Perkins, D.D.: Finding common ground: the importance of place attachment to community participation and planning. J. Plan. Lit. 20, 335–350 (2006)
Acknowledgments.
This work was supported by the EC-funded research and innovation programme H2020 Mindspaces: “Art-driven adaptive outdoors and indoors design” under the grant agreement No.825079.
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Pistola, T. et al. (2022). Imageability-Based Multi-modal Analysis of Urban Environments for Architects and Artists. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_18
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