Name
Title | ||
|---|---|---|
Personalized Photograph Ranking and Selection System Considering Positive and Negative User Feedback |
Abstract | ||
|---|---|---|
In this article, we propose a novel personalized ranking system for amateur photographs. The proposed framework treats the photograph assessment as a ranking problem and we introduce the idea of personalized ranking, which ranks photographs considering both their aesthetic qualities and personal preferences. Photographs are described using three types of features: photo composition, color and intensity distribution, and personalized features. An aesthetic prediction model is learned from labeled photographs by using the proposed image features and RBF-ListNet learning algorithm. The experimental results show that the proposed framework outperforms in the ranking performance: a Kendall's tau value of 0.432 is significantly higher than those obtained by the features proposed in one of the state-of-the-art approaches (0.365) and by learning based on support vector regression (0.384). To realize personalization in ranking, three approaches are proposed: the feature-based approach allows users to select photographs with specific rules, the example-based approach takes the positive feedback from users to rerank the photograph, and the list-based approach takes both positive and negative feedback from users into consideration. User studies indicate that all three approaches are effective in both aesthetic and personalized ranking. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2584105 | TOMCCAP |
Keywords | Field | DocType |
photograph ranking,algorithms,aesthetic rules,applications,example-based reranking,graphical user interfaces,personalized ranking,relevance feedback,photograph composition,performance | Relevance feedback,Information retrieval,Ranking,Ranking SVM,Feature (computer vision),Computer science,Support vector machine,User studies,Multimedia,Personalization | Journal |
Volume | Issue | ISSN |
10 | 4 | 1551-6857 |
Citations | PageRank | References |
7 | 0.49 | 37 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Che-Hua Yeh | 1 | 107 | 7.59 |
| Brian A. Barsky | 2 | 585 | 361.48 |
| Ming Ouhyoung | 3 | 1609 | 266.64 |