Abstract
Based on the existent computational aesthetic measurements, we present a new approach that combining both saliency region detection and extraction with a feature set in line with the principle of human vision. We first extract the saliency region using frequency-based method, then extract 53 features from both local and global regions, and select top 15 features which can determine the best aesthetic value. We run both SVM classification & regression and CART as well as linear regression on the filtered dataset. The experiments show a meaningful result of an accuracy above 70%.
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Zhou, Y., Tan, Y., Li, G. (2014). Computational Aesthetic Measurement of Photographs Based on Multi-features with Saliency. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_39
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DOI: https://doi.org/10.1007/978-3-319-09333-8_39
Publisher Name: Springer, Cham
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