To the best of our knowledge, there is no prior dataset specifically constructed for composition assessment. To support the research on this task, we build a dataset upon the existing AADB dataset, from which we collect a total of 9,958 real-world photos. We adopt a composition rating scale from 1 to 5, where a larger score indicates better composition. We make annotation guidelines for composition quality rating and train five individual raters who specialize in fine art. So for each image, we can obtain five composition scores ranging from 1 to 5. Given the subjective nature of human aesthetic activity, we perform sanity check and consistency analysis. We use 240 additional “sanity check” images during annotating to roughly verify the validness of our annotations. We also examine the consistency of composition ratings provided by five individual raters (see Supplementary). We average the composition scores as the ground-truth composition mean score for each image. More details about our CADB dataset will be elaborated in Supplementary. Besides, we observe the content bias in our CADB dataset, that is, there are some biased categories whose score distributions are concentrated in a very narrow interval. After removing 461 biased images, we split the remaining images into 8,547 training images and 950 test images, in which the test set is made less biased for better evaluation (see Supplementary).

Cited from "Image Composition Assessment with Saliency-augmented Multi-pattern Pooling" Zhang, Bo and Niu, Li and Zhang, Liqing

Citation

@article{zhang2021image, title={Image Composition Assessment with Saliency-augmented Multi-pattern Pooling}, author={Zhang, Bo and Niu, Li and Zhang, Liqing}, journal={arXiv preprint arXiv:2104.03133}, year={2021} }