AADB

This dataset is a new aesthetics and attributes database (AADB) which contains aesthetic scores and meaningful attributes assigned to each image by multiple human raters. Anonymized rater identities are recorded across images allowing us to exploit intra-rater consistency using a novel sampling strategy when computing the ranking loss of training image pairs. We show the proposed sampling strategy is very effective and robust in face of subjective judgement of image aesthetics by individuals with different aesthetic tastes. Experiments demonstrate that our unified model can generate aesthetic rankings that are more consistent with human ratings. To further validate our model, we show that by simply thresholding the estimated aesthetic scores, we are able to achieve state-or-the-art classification performance on the existing AVA dataset benchmark. To collect a large and varied set of photographic images, we download images from the Flickr website1 which carry a Creative Commons license and manually curate the data set to remove non-photographic images (e.g. cartoons, drawings, paintings, ads images, adult-content images, etc.). We have five different workers then independently annotate each image with an overall aesthetic score and a fixed set of eleven meaningful attributes using Amazon Mechanical Turk (AMT)2 . The AMT raters work on batches, each of which contains ten images. For each image, we average the ratings of five raters as the ground-truth aesthetic score. The number of images rated by a particular worker follows long tail distribution.

本数据集为全新的美学与属性数据库（Aesthetics and Attributes Database, AADB），包含多张图像的美学评分与多名人类标注人员赋予的有意义属性标签。所有标注人员的匿名身份会跨图像记录，使得我们在计算训练图像对的排序损失时，可通过新颖的采样策略利用标注人员的内部一致性。我们证实，所提出的采样策略在面对不同审美偏好个体对图像美学的主观评判时，仍具备出色的有效性与鲁棒性。实验结果表明，我们的统一模型所生成的美学排序结果与人类评分的一致性更高。为进一步验证我们的模型，我们证实仅通过对估计得到的美学评分设置简单阈值，即可在现有AVA数据集基准上实现当前最优的分类性能。为收集大规模且多样化的摄影图像集，我们从持有知识共享（Creative Commons）许可的Flickr网站下载图像，并手动整理数据集以移除非摄影类图像（如卡通、手绘稿、画作、广告图、成人内容图像等）。我们邀请5名不同的标注人员，借助亚马逊众包平台Amazon Mechanical Turk（AMT），为每张图像独立标注整体美学评分与一套固定的11项有意义属性。AMT标注人员以批次形式开展标注工作，每个批次包含10张图像。针对每张图像，我们将5名标注人员的评分取平均值作为其真实美学评分。单名标注人员所标注的图像数量服从长尾分布（long-tail distribution）。

搜集汇总

背景与挑战

背景概述

AADB数据集是一个美学与属性数据库，包含从Flickr收集的摄影图像，每张图像由多名标注者通过Amazon Mechanical Turk独立标注整体美学分数和11个属性，评分取平均值作为真实值。该数据集设计用于训练美学评分模型，通过记录匿名标注者身份和采用新颖采样策略，提高模型在个体审美差异下的鲁棒性，实验表明其模型能生成更符合人类评分的美学排名，并在现有AVA数据集基准上达到先进分类性能。