Data-Driven Segmentation of Image Editing Logs

with Zipeng Liu and Tamara Munzner

Automatic segmentation of logs for creativity tools such as image editing systems could improve their usability and learn-ability 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 re-touching, 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 Photo-shop 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.


Data-Driven Multi-level Segmentation of Image Editing Logs
PDF (CHI 2020)

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