ABSTRACT
Digital photo management is becoming indispensable for the explosively growing family photo albums due to the rapid popularization of digital cameras and mobile phone cameras. In an effective photo management system photo annotation is the most challenging task. In this paper, we develop several innovative interaction techniques for semi-automatic photo annotation. Compared with traditional annotation systems, our approach provides the following new features: "cluster annotation" puts similar faces or photos with similar scene together, and enables user label them in one operation; "contextual re-ranking" boosts the labeling productivity by guessing the user intention; "ad hoc annotation" allows user label photos while they are browsing or searching, and improves system performance progressively through learning propagation. Our results show that these technologies provide a more user friendly interface for the annotation of person name, location, and event, and thus substantially improve the annotation performance especially for a large photo album.
- iView Media Pro. http://www.iview-multimedia.com.Google Scholar
- ACDSee. http://www.acdsee.com.Google Scholar
- Microsoft Diginal Image Suite. http://www.microsoft.com/products/imaging.Google Scholar
- Picasa. http://picasa.google.com.Google Scholar
- Photoshop Elements. http://www.adobe.com/products/photoshopelwin.Google Scholar
- Flickr. http://www.flickr.com.Google Scholar
- ESP Game. http://espgame.org.Google Scholar
- Google Image Labeler. http://images.google.com/imagelabeler.Google Scholar
- Riya. http://www.riya.com.Google Scholar
- T. Ahonen, A. Hadid, and M. Pietikainen. Face recognition with local binary patterns. In Proc. the 8th European Conference on Computer Vision, 2004.Google ScholarCross Ref
- F. Bach and M. Jordan. Learning spectral clustering. In Proc. of Neural Info. Processing Systems, 2003.Google Scholar
- L. Chen, B. Hu, L. Zhang, M. Li, and H. Zhang. Face annotation for family photo album management. International Journal of Image and Graphics (IJIG), Special Issue on Multimedia Data Storage and Management, 3, 2003.Google Scholar
- A. Girgensohn, J. Adcock, and L. Wilcox. Leveraging face recognition technology to find and organize photos. In MIR '04: Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval, pages 99--106, New York, NY, USA, 2004. ACM Press. Google ScholarDigital Library
- A. Graham, H. Garcia-Molina, A. Paepcke, and T. Winograd. Time as essence for photo browsing through personal digital libraries. In Joint Conference on Digital Libraries, pages 326--335, New York, NY, USA, 2002. ACM Press. Google ScholarDigital Library
- A. Kuchinsky, C. Pering, M. L. Creech, D. Freeze, B. Serra, and J. Gwizdka. Fotofile: a consumer multimedia organization and retrieval system. In CHI '99: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 496--503, New York, NY, USA, 1999. ACM Press. Google ScholarDigital Library
- J. Platt. Autoalbum: Clustering digital photographs using probabalistic model merging, 2000.Google Scholar
- J. C. Platt, M. Czerwinski, and B. A. Field. Phototoc: Automatic clustering for browsing personal photographs, 2002.Google Scholar
- K. Rodden and K. R. Wood. How do people manage their digital photographs? In CHI '03: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 409--416, New York, NY, USA, 2003. ACM Press. Google ScholarDigital Library
- B. Shneiderman and H. Kang. Direct annotation: A drag-and-drop strategy for labeling photos. In Proc. of IEEE Conf. on Information Visualization, 2000. Google ScholarDigital Library
- B. Suh and B. Bederson. Semi-automatic image annotation using event and torso identification. Technical report, Computer Science Department, University of Maryland, College Park, MD, 2004.Google Scholar
- P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, volume 1, pages 511--518, 2001.Google ScholarCross Ref
- L. von Ahn and L. Dabbish. Labeling images with a computer game. In CHI '04: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 319--326, New York, NY, USA, 2004. ACM Press. Google ScholarDigital Library
- X. Wang and X. Tang. A unified framework for subspace face recognition. IEEE Trans. Pattern Anal. Mach. Intell., 26(9):1222--1228, 2004. Google ScholarDigital Library
- X. Wang and X. Tang. Random sampling for subspace face recognition. Int. J. Comput. Vision, 70(1):91--104, 2006. Google ScholarDigital Library
- L. Zhang, L. Chen, M. Li, and H. Zhang. Automated annotation of human faces in family albums. In Proc. of ACM Multimedia, 2003. Google ScholarDigital Library
- L. Zhang, Q. Yang, T. Bao, D. Vronay, and X. Tang. Imlooking: image-based face retrieval in online dating profile search. In CHI '06: CHI '06 extended abstracts on Human factors in computing systems, pages 1577--1582, New York, NY, USA, 2006. ACM Press. Google ScholarDigital Library
- W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld. Face recognition: A literature survey. ACM Comput. Surv., 35(4):399--458, 2003. Google ScholarDigital Library
- Y. Zhou, L. Gu, and H.-J. Zhang. Bayesian tangent shape model: Estimating shape and pose parameters via bayesian inference. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2003. Google ScholarDigital Library
- Y. Zhou, W. Zhang, X. Tang, and H. Shum. A bayesian mixture model for multi-view face alignment. In CVPR '05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) -- Volume 2, pages 741--746, Washington, DC, USA, 2005. IEEE Computer Society. Google ScholarDigital Library
Index Terms
- EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking
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