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首页> 外文期刊>IEEE transactions on multimedia >Distribution-Oriented Aesthetics Assessment With Semantic-Aware Hybrid Network
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Distribution-Oriented Aesthetics Assessment With Semantic-Aware Hybrid Network

机译：语义感知混合网络的面向分布美学评估

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摘要

Image aesthetics assessment has emerged as a hot topic in recent years due to its potential in numerous high-level vision applications. In this paper, distinguished from existing studies relying on a single label, we propose quantifying image aesthetics by a distribution over multiple quality levels. The distribution-based representation characterizes the disagreement among users' aesthetic preferences regarding the same image, and is also compatible with the traditional task of aesthetic label prediction. Our framework is developed based on fully convolutional networks and enables inputs of varying sizes. In this way, we circumvent the fixed-size constraint of prevalent convolutional neural networks, and avoid the risk of impairing the intrinsic aesthetic appeal of images. Moreover, given the fact that aesthetic perceiving is coupled with semantic understanding, we present a novel semantic-aware hybrid NEtwork (SANE), which harvests the information from object categorization and scene recognition to enhance image aesthetics assessment. Experiments on two benchmark datasets have well verified the effectiveness of our approach in both scenarios of aesthetic distribution prediction and aesthetic label prediction, and highlighted the benefits of input preserving as well as semantic understanding for images.
机译：图像美学评估由于其在众多高级视觉应用中的潜力而成为近年来的热门话题。在本文中，与现有的依赖于单个标签的研究不同，我们提出了通过在多个质量级别上分布来量化图像美感的方法。基于分布的表示法表征了用户对于同一张图片的审美偏好之间的分歧，并且还与审美标签预测的传统任务兼容。我们的框架是基于完全卷积网络开发的，并且可以输入各种大小。这样，我们规避了流行的卷积神经网络的固定大小约束，并避免了损害图像固有的美学吸引力的风险。此外，鉴于审美感知与语义理解相结合的事实，我们提出了一种新颖的语义感知混合NEtwork（SANE），它从对象分类和场景识别中收集信息，以增强图像美学评估。在两个基准数据集上进行的实验已经很好地验证了我们的方法在审美分布预测和审美标签预测这两种情况下的有效性，并强调了输入保留和图像语义理解的好处。

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