TY - GEN
T1 - Learning user for interest for image browsing on small-form-factor devices
AU - Xie, Xing
AU - Lin, Hao
AU - Goumaz, Simon
AU - Ma, Wei-Yine
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - Mobile devices which can capture and view pictures are becoming increasingly common in our life. The limitation of these small-form-factor devices makes the user experience of image browsing quite different from that on desktop PCs. In this paper, we first present a user study on how users interact with a mobile image browser with basic functions. We found that on small displays, users tend to use more zooming and scrolling actions in order to view interesting regions in detail. From this fact, we designed a new method to detect user interest maps and extract user attention objects from the image browsing log. This approach is more efficient than image-analysis based methods and can better represent users' actual interest. A smart image viewer was then developed based on user interest analysis. A second experiment was carried out to study how users behave with such a viewer. Experimental results demonstrate that the new smart features can improve the browsing efficiency and are a good compliment to traditional image browsers. Copyright 2005 ACM.
AB - Mobile devices which can capture and view pictures are becoming increasingly common in our life. The limitation of these small-form-factor devices makes the user experience of image browsing quite different from that on desktop PCs. In this paper, we first present a user study on how users interact with a mobile image browser with basic functions. We found that on small displays, users tend to use more zooming and scrolling actions in order to view interesting regions in detail. From this fact, we designed a new method to detect user interest maps and extract user attention objects from the image browsing log. This approach is more efficient than image-analysis based methods and can better represent users' actual interest. A smart image viewer was then developed based on user interest analysis. A second experiment was carried out to study how users behave with such a viewer. Experimental results demonstrate that the new smart features can improve the browsing efficiency and are a good compliment to traditional image browsers. Copyright 2005 ACM.
KW - Attention model
KW - Mobile image browsing
KW - Small display
UR - http://www.scopus.com/inward/record.url?scp=80051471929&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-80051471929&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1581139985
SN - 9781581139983
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 671
EP - 680
BT - CHI 2005: Technology, Safety, Community: Conference Proceedings - Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - CHI 2005: Technology, Safety, Community - Conference on Human Factors in Computing Systems
Y2 - 2 April 2005 through 7 April 2005
ER -