Content-Based Photo Quality Assessment

Automatically assessing photo quality from the perspective of visual aesthetics is of great interest in high-level vision research and has drawn much attention in recent years. In this paper, we propose content-based photo quality assessment using both regional and global features. Under this framew...

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Veröffentlicht in:	IEEE transactions on multimedia Jg. 15; H. 8; S. 1930 - 1943
Hauptverfasser:	Tang, Xiaoou, Luo, Wei, Wang, Xiaogang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.12.2013 Institute of Electrical and Electronics Engineers
Schlagworte:	Applied sciences Benchmark testing Clarity contrast composition geometry Computer science; control theory; systems content-based dark channel Data processing. List processing. Character string processing Exact sciences and technology Feature extraction Geometry hue composition Information systems. Data bases Memory organisation. Data processing photo quality assessment Quality assessment scene composition Software Training Visualization Computer vision Landscape Internet protocol hue composition Very large databases Online algorithm Ground truth scene composition Adaptive method Visual perception content-based dark channel Classification Quality control Content-based retrieval Clarity contrast Aesthetics photo quality assessment Pattern extraction composition geometry Visual control
ISSN:	1520-9210, 1941-0077
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Abstract	Automatically assessing photo quality from the perspective of visual aesthetics is of great interest in high-level vision research and has drawn much attention in recent years. In this paper, we propose content-based photo quality assessment using both regional and global features. Under this framework, subject areas, which draw the most attentions of human eyes, are first extracted. Then regional features extracted from both subject areas and background regions are combined with global features to assess photo quality. Since professional photographers adopt different photographic techniques and have different aesthetic criteria in mind when taking different types of photos (e.g., landscape versus portrait), we propose to segment subject areas and extract visual features in different ways according to the variety of photo content. We divide the photos into seven categories based on their visual content and develop a set of new subject area extraction methods and new visual features specially designed for different categories. The effectiveness of this framework is supported by extensive experimental comparisons of existing photo quality assessment approaches as well as our new features on different categories of photos. In addition, we propose an approach of online training an adaptive classifier to combine the proposed features according to the visual content of a test photo without knowing its category. Another contribution of this work is to construct a large and diversified benchmark dataset for the research of photo quality assessment. It includes 17,673 photos with manually labeled ground truth. This new benchmark dataset can be down loaded at http://mmlab.ie.cuhk.edu.hk/CUHKPQ/Dataset.htm.
AbstractList	Automatically assessing photo quality from the perspective of visual aesthetics is of great interest in high-level vision research and has drawn much attention in recent years. In this paper, we propose content-based photo quality assessment using both regional and global features. Under this framework, subject areas, which draw the most attentions of human eyes, are first extracted. Then regional features extracted from both subject areas and background regions are combined with global features to assess photo quality. Since professional photographers adopt different photographic techniques and have different aesthetic criteria in mind when taking different types of photos (e.g., landscape versus portrait), we propose to segment subject areas and extract visual features in different ways according to the variety of photo content. We divide the photos into seven categories based on their visual content and develop a set of new subject area extraction methods and new visual features specially designed for different categories. The effectiveness of this framework is supported by extensive experimental comparisons of existing photo quality assessment approaches as well as our new features on different categories of photos. In addition, we propose an approach of online training an adaptive classifier to combine the proposed features according to the visual content of a test photo without knowing its category. Another contribution of this work is to construct a large and diversified benchmark dataset for the research of photo quality assessment. It includes 17,673 photos with manually labeled ground truth. This new benchmark dataset can be down loaded at http://mmlab.ie.cuhk.edu.hk/CUHKPQ/Dataset.htm.
Author	Luo, Wei Wang, Xiaogang Tang, Xiaoou
Author_xml	– sequence: 1 givenname: Xiaoou surname: Tang fullname: Tang, Xiaoou email: xtang@ie.cuhk.edu.hk organization: Department of Information Engineering, Chinese University of Hong Kong, Hong Kong – sequence: 2 givenname: Wei surname: Luo fullname: Luo, Wei email: awesomekeane@gmail.com organization: Department of Information Engineering, Chinese University of Hong Kong – sequence: 3 givenname: Xiaogang surname: Wang fullname: Wang, Xiaogang email: xgwang@ee.cuhk.edu.hk organization: Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong
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SubjectTerms	Applied sciences Benchmark testing Clarity contrast composition geometry Computer science; control theory; systems content-based dark channel Data processing. List processing. Character string processing Exact sciences and technology Feature extraction Geometry hue composition Information systems. Data bases Memory organisation. Data processing photo quality assessment Quality assessment scene composition Software Training Visualization
Title	Content-Based Photo Quality Assessment
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