Abstract
The aim of this paper is to reduce to a minimum the level of human intervention in the semantic annotation process of images. Ideally, only one copy of each object of interest would be labeled manually, and the labels would then be propagated automatically to all other occurrences of the objects in the database. To that end, we propose a neighbor-based influence propagation approach KProp which builds a voting model and propagates the knowledge associated to some objects to similar objects. We show that KProp can perform efficiently through matrix computations and achieve better performance with fewer labeled examples per object.
Original language | English (US) |
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Title of host publication | MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops |
Pages | 1033-1036 |
Number of pages | 4 |
DOIs | |
State | Published - Dec 29 2011 |
Externally published | Yes |
Event | 19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 - Scottsdale, AZ, United States Duration: Nov 28 2011 → Dec 1 2011 |
Other
Other | 19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 |
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Country/Territory | United States |
City | Scottsdale, AZ |
Period | 11/28/11 → 12/1/11 |
All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design
- Human-Computer Interaction