| Summary: | 碩士 === 臺灣大學 === 資訊網路與多媒體研究所 === 98 === Due to the growing popularity and availability of digital camera, the number of photos owned by each user has increased dramatically. Consequently, manually selecting favorite photos becomes nearly impractical. Moreover, since most digital camera users may not be familiar with the photography rules used by professional photographers, a number of studies on automatic photo selection have been conducted. Most early researches use low-level features (clarity, brightness and saturation) and achieve reasonably good results in binary classification (Ke 2006, 72% accuracy, Datta 2006, 86% accuracy). Recent researches have combined high-level features formulated from photographers’ rules of photo composition with low-level features to produce a better result (Luo 2008, 95% accuracy). However, most of previous researches follow the same framework: training a predication model with some of the data and testing the model with the rest. The problem of this framework is that the prediction model may reflect the preference of a certain group of people but it may not agree with each individual user’s taste. Besides, previous works concentrate on the predication accuracy of binary classification, but we also want to examine the accuracy of ordering of the entire ranked list. Therefore, we create a system for users to combine a ranked photo list based on photography rules with their personal preferences to create more personalized results. Our system has been able to achieve (1) 93 % binary classification accuracy (2) 0.4054 Kendall Tau correlation rank (3) 92% satisfaction rate of personalized re-ranked list over original list.
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