개인화 모델 기반 이미지의 심미적 퀄리티 평가
Image Aesthetic Quality Assessment: Personalization by Interactions
- 주제(키워드) Support vector machine , image aesthetic quality , interactive learning
- 발행기관 포항공과대학교 일반대학원
- 지도교수 Bohyung Han
- 발행년도 2015
- 학위수여년월 2015. 2
- 학위명 석사
- 학과 및 전공 일반대학원 창의IT융합공학과
- 세부전공 Computer Vision
- 원문페이지 45
- 실제URI http://www.dcollection.net/handler/postech/000001914014
- 본문언어 영어
- 저작권 포항공과대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
We propose an image aesthetic quality assessment algorithm, which considers personal taste in addition to generally perceived preference. This problem is formulated by a combination of two different learning frameworks based on support vector machines—Support Vector Regression (SVR) and Ranking SVM (R-SVM), where SVR learns a general model based on public datasets and R-SVM adjusts the model to accommodate personal preference obtained from user interactions. The combined framework, called R-SVR, is represented by a single objective function, which is optimized jointly to learn a model for personalized image aesthetic quality assessment. For the optimization, we use only a small subset of public dataset identified by k-nearest neighbor search instead of using all available training data. This strategy is useful in practice because it reduces training time significantly and alleviates data imbalance problem between regression and ranking. The proposed algorithm is tested through simulation and user study, and we present that our interactive learning algorithm by R-SVR is effective to increase user’s satisfaction and improve prediction performance.
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