Abstract
This paper proposes a robust method of viewpoint assessment for taking a good photograph of architecture. Unlike the conventional works devoted to assessing the aesthetic of a photograph mainly relying on the image features, both the image and geometric features are extracted from the architecture photos in our method. Furthermore, we explore the mutual knowledge between these two aspects of features with multi-view learning. With the learner trained by multi-view learning, the viewpoint goodness of architecture photograph can be assessed by either aspect of the features. Experiments suggest that the multi-view learning with kernel canonical correlation analysis achieves superior performance over using solely traditional image features. With the help of multi-view learning, we can harness the geometric cues with image features effectively.
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Acknowledgments
This work is supported in part by the Natural Science Foundation of Jiangsu Province under Grants BK20150016, the National Natural Science Foundation of China under Grants 61772257, 61502005, 61672279, the Fundamental Research Funds for the Central Universities 020214380042, and the Anhui Science Foundation under Grant 1608085QF129.
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He, J. et al. (2018). Multi-view Viewpoint Assessment for Architectural Photos. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_46
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DOI: https://doi.org/10.1007/978-3-030-00764-5_46
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