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研究生:羅力勻
研究生(外文):Li-Yun Lo
論文名稱:基於色彩及紋理特徵分布之照片品質評估系統
論文名稱(外文):Photo quality assessment based on color and texture distribution
指導教授:陳洳瑾
指導教授(外文):Ju-Chih Chen
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:資訊工程系
學門:教育學門
學類:專業科目教育學類
論文種類:學術論文
論文出版年:101
畢業學年度:100
語文別:中文
論文頁數:44
中文關鍵詞:特徵空間分佈高斯混合模型
外文關鍵詞:Color momentHOGContext modeling
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基於攝影產業的發達,本研究提出一個攝影照片評分系統,可自動評斷攝影照片品質的高低。針對此議題已有許多相關的研究方法被提出,以往過去是將畫面幾何構圖、配色、情感表達、視覺重點的選擇等分別討論,也提出了許多概念式的通則,本研究希望綜合上述特徵共同討論其重要性。
本研究在網路上蒐集許多包含攝影專家(high quality photo)和一般使用者(low quality photo)的照片當作訓練資料,擷取其色彩(color)及紋理(texture)特徵,並分析特徵在相片空間中的分佈情形,利用高斯混合模型建立特徵分佈的模組當作評斷照片品質的標準。本研究提出一個將構圖結合色彩、紋理特徵以及考慮全景與照片區域差異的分析照片美學的系統,透過特徵擷取和使用高斯混合模型將特徵在照片中的位置分佈模組化,利用此特徵模組分析測試影像的特徵分佈,藉此分析結果判定測試影像是否專家照片的特徵。
This study proposes a photo quality assessment based on the spatial relations of image patches. In order to investigate the components of high-quality photos, the image is decomposed into patches based on the color information. Then the color moment and histogram of oriented gradients (HOG) are extracted for the feature representation. Because the diverse types of photos, the photo with the segmented patches is assigned to a subtopic before further modeling. Different from the prior researches which model the spatial relations of image patches obtained from high quality photo, in our work the negative models are learned from the low quality photos as well to provide more discriminate assessment results. Note that the spatial information of location and size of image patch is modeled by Gaussian mixture model (GMM), and the likelihood probabilities in accordance with the positive and negative context models are integrated as the assessment score. The experimental results demonstrates that the usage of the low-quality photos can provide the significant improvement and the proposed system have the promising potential for the photo quality assessment.
摘 要 i
ABSTRACT ii
誌 謝 iii
目錄 iv
圖目錄 vi
一、 導論 1
1.1 研究動機 1
1.2 研究貢獻 5
1.3 研究架構 5
二、 文獻探討 6
2.1 照片品質評分之系統 6
2.2 尋找畫面最佳視角為輸出之分析系統 7
三、 系統流程 11
3.1 系統架構 12
四、 照片評分系統 13
4.1 影像分割 (Image segmentation) 13
4.2 特徵擷取 (Feature extraction) 14
4.2.1 顏色特徵 (Color feature) 14
4.2.2 紋理特徵 (Texture feature) 16
4.3 代表性區塊生成與照片種類分群 (Patches and subtopics clustering) 20
4.4 利用照片空間資訊建立特徵模組 (Statistic modeling with coupled spatial relations) 23
4.5 照片品質辨識 (photo quality assessment) 25
五、 實驗結果 28
5.1 實驗資料集各項參數設定 28
5.1.1 實驗資料 28
5.1.2 實驗參數設定 28
5.1.3 系統效能 29
六、 結論 32
參考文獻 33
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