摘要
使用相似照片進行照片品質評估
莊淳卉

照片品質評估問題是對於照片的美感來給照片打分數，在之前的研究中，對於照片品質評估都是從單張照片擷取特徵值。有別以往的實驗，在本論文中我們提出使用谷歌圖片搜索 (Google Image Search) 以圖搜圖找出相似照片來提升照片排名準確度。我們提出了從一組類似的照片中提取新的特徵值：相對 (relative) 特徵值和標準差 (standard deviation) 特徵值來輔助照片評分。我們使用兩個回歸模型：支持向量回歸（SVR）和排序支持向量機（Ranking SVM） 從視覺特徵學習出評分器去預測照片的分數。在實驗中，我們從Photo.net 收集9,000照片，每張照片使用Google Image Search搜索前10張相似的圖像，在我們的資料庫中總共有99,000張圖像。我們研究哪些種類的照片用類似的圖像有利於照片評分。

關鍵字：支持向量回歸、排序支持向量機、照片品質評估

ABSTRACT

Photo Quality Assessment with Similar Images

by

Chuang Chun-Hui

In this paper, we explore using Google Image Search that finds multiple similar images to facilitate the photo quality assessment problem. In particular, we present relative and standard deviation features that are extracted from a group of similar images. We further evaluate two regression models: support vector regression (SVR) and ranking support vector machine (RankSVM) to predict aesthetic scores from visual features. In the experiment, we collected 9,000 images from Photo.net, and for each image we collected 10 similar images using Google Image Search. We examine and identify cases where similar images would benefit the photo quality assessment task.

Keywords: support vector regression; ranking support vector machine; photo quality assessment.

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