Content-Based Photo Quality Assessment
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AbstractAutomatically 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.
All Author(s) ListTang XO, Luo W, Wang XG
Journal nameIEEE Transactions on Multimedia
Year2013
Month12
Day1
Volume Number15
Issue Number8
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1930 - 1943
ISSN1520-9210
eISSN1941-0077
LanguagesEnglish-United Kingdom
KeywordsClarity contrast; composition geometry; content-based; dark channel; hue composition; photo quality assessment; scene composition.
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Computer Science, Software Engineering; COMPUTER SCIENCE, SOFTWARE ENGINEERING; Telecommunications; TELECOMMUNICATIONS

Last updated on 2020-20-11 at 01:02