Data-driven Multi-level Segmentation of Image Editing Logs

要旨

Automatic segmentation of logs for creativity tools such as image editing systems could improve their usability and learnability by supporting such interaction use cases as smart history navigation or recommending alternative design choices. We propose a multi-level segmentation model that works for many image editing tasks including poster creation, portrait retouching, and special effect creation. The lowest-level chunks of logged events are computed using a support vector machine model and higher-level chunks are built on top of these, at a level of granularity that can be customized for specific use cases. Our model takes into account features derived from four event attributes collected in realistically complex Photoshop sessions with expert users: command, timestamp, image content, and artwork layer. We present a detailed analysis of the relevance of each feature and evaluate the model using both quantitative performance metrics and qualitative analysis of sample sessions.

キーワード
Log segmentation
Image editing logs
Interaction history
Multi-level hierarchy
著者
Zipeng Liu
University of British Columbia, Vancouver, BC, Canada
Zhicheng Liu
Adobe Research, Seattle, WA, USA
Tamara Munzner
University of British Columbia, Vancouver, BC, Canada
DOI

10.1145/3313831.3376152

論文URL

https://doi.org/10.1145/3313831.3376152

動画

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Photo & video manipulation

Paper session
312 NI'IHAU
5 件の発表
2020-04-28 23:00:00
2020-04-29 00:15:00
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