Distribution-oriented Aesthetics Assessment for Image Search

被引：15

作者：

Cui, Chaoran ^{[1
]}

Fang, Huidi ^{[2
]}

Deng, Xiang ^{[2
]}

Nie, Xiushan ^{[1
]}

Dai, Hongshuai ^{[3
]}

Yin, Yilong ^{[2
]}

机构：

[1] Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China

[2] Shandong Univ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China

[3] Shandong Univ Finance & Econ, Sch Stat, Jinan, Shandong, Peoples R China

来源：

SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL | 2017年

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

aesthetics assessment; label distribution learning; image search;

D O I：

10.1145/3077136.3080704

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Aesthetics has become increasingly prominent for image search to enhance user satisfaction. Therefore, image aesthetics assessment is emerging as a promising research topic in recent years. In this paper, distinguished from existing studies relying on a single label, we propose to quantify the image aesthetics by a distribution over quality levels. The distribution representation can effectively characterize the disagreement among the aesthetic perceptions of users regarding the same image. Our framework is developed on the foundation of label distribution learning, in which the reliability of training examples and the correlations between quality levels are fully taken into account. Extensive experiments on two benchmark datasets well verified the potential of our approach for aesthetics assessment. The role of aesthetics in image search was also rigorously investigated.

引用

页码：1013 / 1016

页数：4

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