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Conference Paper: Data-driven adaptive history for image editing

TitleData-driven adaptive history for image editing
Authors
Issue Date2016
PublisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://dl.acm.org/event.cfm?id=RE299&tab=pubs
Citation
I3D '16: Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, Redmond, WA, USA, 26-28 February 2016 , p. 103-111 How to Cite?
AbstractDigital 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.
Persistent Identifierhttp://hdl.handle.net/10722/224909
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChen, HT-
dc.contributor.authorWei, LY-
dc.contributor.authorHartmann, B-
dc.contributor.authorAgrawala, M-
dc.date.accessioned2016-04-18T03:33:58Z-
dc.date.available2016-04-18T03:33:58Z-
dc.date.issued2016-
dc.identifier.citationI3D '16: Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, Redmond, WA, USA, 26-28 February 2016 , p. 103-111-
dc.identifier.issn1931-4027-
dc.identifier.urihttp://hdl.handle.net/10722/224909-
dc.description.abstractDigital 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.-
dc.languageeng-
dc.publisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://dl.acm.org/event.cfm?id=RE299&tab=pubs-
dc.relation.ispartofACM SIGGRAPH Symposium on Interactive 3D Graphics and Games Proceedings-
dc.rightsACM SIGGRAPH Symposium on Interactive 3D Graphics and Games Proceedings. Copyright © Association for Computing Machinery, Inc.-
dc.titleData-driven adaptive history for image editing-
dc.typeConference_Paper-
dc.identifier.emailWei, LY: lywei@cs.hku.hk-
dc.identifier.authorityWei, LY=rp01528-
dc.identifier.doi10.1145/2856400.2856417-
dc.identifier.hkuros257417-
dc.identifier.spage103-
dc.identifier.epage111-
dc.publisher.placeUnited States-

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