Content-based photo quality assessment.

• Other Authors: Luo, Wei.
• Format: Others
• Language: English; Chinese
• Published: 2012
• Subjects: Visual perception > Data processing; Image processing > Data processing; Aesthetics
• Online Access: http://library.cuhk.edu.hk/record=b5549070; http://repository.lib.cuhk.edu.hk/en/item/cuhk-328641

基於審美的圖像質量自動評估近年來引起了計算機視覺領域的普遍關注。在這篇論文裡, 我們提出使用局部與整體特徵, 基於圖像內容進行圖片質量評估。首先, 圖像的主題區域被提取出來。這部分區域最吸引觀看者的注意力。基於主題區域, 我們提取局部特徵, 並結合整體特徵進行圖像質量評估。攝影專家拍攝圖片時, 對於不同內容的圖片, 會採取不同的技術手段和審美衡量標準。基於此項觀察, 我們提出根據圖片的內容, 在提取主題區域以及特徵的時候採用不同的手段。我們講數據根據圖像內容分為七類, 並分別設計主題區域提取方法和設計特徵。我們通過翔實的實驗數據，證明提出的框架之有效。 === 同時, 我們提出根據圖像內容特徵構建自適應分類器, 以在不事先知道圖像內容分類的情況下進行自動質量評估, 並取得滿意效果。 === 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 both from 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 the irvisual content and develop a set of new subject are a extraction methods and new visual features specially designed for different categories. === This argument is supported by extensive experimental comparisons of existing photo quality assessment approaches as well as our new features over 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 database for the research of photo quality assessment. It includes 17, 613 photos with manually labeled ground truth. This new benchmark database will be released to the research community. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Luo, Wei. === Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. === Includes bibliographical references (leaves 47-52). === Abstracts also in Chinese. === Chapter Abstract --- p.i === Chapter Acknowledgement --- p.iv === Chapter 1 --- Introduction --- p.1 === Chapter 1.1 --- Photo Quality Assessment by Professionals --- p.2 === Chapter 1.2 --- Automatic Quality Assessment --- p.6 === Chapter 1.3 --- Our Approach --- p.8 === Chapter 2 --- RelatedWork --- p.12 === Chapter 3 --- Content-based Quality Assessment --- p.15 === Chapter 3.1 --- Global Features --- p.15 === Chapter 3.1.1 --- Hue Composition Feature --- p.15 === Chapter 3.1.2 --- Scene Composition Feature --- p.19 === Chapter 3.2 --- Subject Area Extraction Methods --- p.21 === Chapter 3.2.1 --- Clarity-Based Subject Area Extraction --- p.22 === Chapter 3.2.2 --- Layout-Based Subject Area Extraction --- p.25 === Chapter 3.2.3 --- Human-Based Subject Area Extraction --- p.25 === Chapter 3.3 --- Regional Features --- p.25 === Chapter 3.3.1 --- Dark Channel Feature --- p.27 === Chapter 3.3.2 --- Clarity Contrast Feature --- p.28 === Chapter 3.3.3 --- Lighting Contrast Feature --- p.30 === Chapter 3.3.4 --- Composition Geometry Feature --- p.30 === Chapter 3.3.5 --- Complexity Features --- p.31 === Chapter 3.3.6 --- Human Based Features --- p.31 === Chapter 3.4 --- Quality Assessment without the Information of Photo Categories --- p.33 === Chapter 4 --- Experimental Results --- p.37 === Chapter 4.1 --- Database description --- p.37 === Chapter 4.2 --- Experimental Settings --- p.40 === Chapter 4.3 --- Result Analysis --- p.41 === Chapter 4.4 --- Conclusions and Discussions --- p.44 === Bibliography --- p.47