In this paper, an aesthetic modeling method for scenic photographs is proposed. A bottom-up approach is developed to construct an aesthetic library with bag-of-aesthetics preserving features instead of top-down methods that implement the heuristic guidelines (rule-specific features) listed in the photography literature, which is employed in previous works. The proposed method can cover both implicit and explicit aesthetic features with a learning process. The experimental results show that the proposed features in the library (92.06% in accuracy) outperform the state-of-the-art rule-specific features (83.63% in accuracy) significantly in the aesthetic quality assessment for scenic photos, and the rule-specific features are also proved to be encompassed by the proposed features. Meanwhile, it is observed from experiments that the features extracted for contrast information are more effective than those for absolute information, which is consistent with the properties of human visual systems.