Abstract

Digital image editing is usually an iterative process; users repetitively perform short sequences of operations, as well as undo and redo using history navigation tools. In our collected data, undo, redo and navigation constitute about 9 percent of the total commands and consume a significant amount of user time. Unfortunately, such activities also tend to be tedious and frustrating, especially for complex projects.

We address this crucial issue by adaptive history, a UI mechanism that groups relevant operations together to reduce user workloads. Such grouping can occur at various history granularities. We present two that have been found to be most useful. On a fine level, we group repeating commands patterns together to facilitate smart undo. On a coarse level, we segment commands history into chunks for semantic navigation. The main advantages of our approach are that it is intuitive to use and easy to integrate into any existing tools with text-based history lists. Unlike prior methods that are predominately rule-based, our approach is data-driven, and thus adapts better to common editing tasks which exhibit sufficient diversity and complexity that may defy predetermined rules or procedures.

A user study showed that our system performs quantitatively better than two other baselines, and the participants also gave positive qualitative feedbacks on the system features.


2016_I3D_data-driven_history


By | 2017-05-22T11:07:41+00:00 April 22nd, 2014|Highlight, Research|Comments Off on Data-Driven Adaptive History List for Image Editing

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