Qualitative Organization of Photo Collections via Quartet Analysis and Active Learning
Proceedings of the 45th Graphics Interface Conference on Proceedings of Graphics Interface 2019(2019)
Abstract
We introduce the use of qualitative analysis and active learning to photo album construction. Given a heterogeneous collection of photos, we organize them into a hierarchical categorization tree (C-tree) based on qualitative analysis using quartets instead of relying on conventional, quantitative image similarity metrics. The main motivation is that in a heterogeneous collection, quantitative distances may become unreliable between dissimilar data and there is unlikely a single metric that is well applicable to all data. Our qualitative analysis utilizes multiple distance measures and applies them where reliable comparisons are possible. Then from the C-tree, we develop an active learning scheme for fine-grained photo scene classification, allowing the selection of representative photos for layout construction which better reflects user intent. Finally, the selected photos are laid out in a comic-like arrangement based on a style template library and layout optimization. Experiments demonstrate that our method is efficient, user-centered, and produces photo albums that are more preferable in comparison with previous approaches.
MoreTranslated text
Key words
Active Learning, C-tree, Comic-like Photo Collage
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined