Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
MiAlbum - a system for home photo managemet using the semi-automatic image annotation approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
SmartAlbum: a multi-modal photo annotation system
Proceedings of the tenth ACM international conference on Multimedia
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Metadata creation system for mobile images
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Automatic organization for digital photographs with geographic coordinates
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Leveraging face recognition technology to find and organize photos
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Leveraging context to resolve identity in photo albums
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Proceedings of the 6th ACM international conference on Image and video retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Toponym resolution in social media
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
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Automating the process of semantic annotation of digital personal photographs is a crucial step towards efficient and effective management of this increasingly high volume of content. However, this is still a highly challenging task for the research community. This paper proposes a novel solution. Our solution integrates all contextual information available to and from the users, such as their daily emails, schedules, chat archives, web browsing histories, documents, online news, Wikipedia data, and so forth. We then analyze this information and extract important semantic terms, using them as semantic keyword suggestions for their photos. Those keywords are in the form of named entities, such as names of people, organizations, locations, and date/time as well as high frequency terms. Experiments conducted with 10 subjects and a total of 313 photos proved that our proposed approach can significantly help users with the annotation process. We achieved a 33% gain in annotation time as compared to manual annotation. We also obtained very positive results in the accuracy rate of our suggested keywords.