Rong Yan
ABSTRACT
This paper investigates new approaches to improve the efficiency of manual image annotation and help users to produce better annotation results in a given amount of time. Although important in practice, this issue has rarely been studied in a quantitative way before. To achieve this, we first propose two time models to analyze the annotation process for two popular manual annotation approaches, i.e., tagging and browsing. The complementary properties of these approaches have inspired us to merge them to develop a hybrid annotation algorithms called frequency-based annotation. Our experiments on large-scale multimedia collections have shown that the proposed algorithm can achieve an up to 40% annotation time reduction compared with the baseline methods. In other words, it can produce considerably better results using the same annotation time.
- K. Barnard, P. Duygulu, D. Forsyth, N. de Freitas, D. Blei, and M. Jordan. Matching words and pictures. Journal of Machine Learning Research, 3, 2002. Google Scholar
Digital Library
- G. W. Furnas, T. K. Landauer, L. M. Gomez, and S. T. Dumais. The vocabulary problem in human-system communication. Comm. of the ACM, 30(11):964--971, 1987. Google ScholarDigital Library
- C. Halaschek-Wiener, J. Golbeck, A. Schain, M. Grove, B. Parsia, and J. Hendler. Photostuff-an image annotation tool for the semantic web. In Proc. of 4th international semantic web conference, 2005.Google Scholar
- A. G. Hauptmann, W.-H. Lin, R. Yan, J. Yang, and M.-Y. Chen. Extreme video retrieval: joint maximization of human and computer performance. In Proceedings of the 14th annual ACM international conference on Multimedia} pages 385--394, New York, NY, USA, 2006. ACM Press. Google ScholarDigital Library
- J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 119--126, 2003. Google ScholarDigital Library
- L. S. Kennedy, S.-F. Chang, and I. V. Kozintsev. To search or to label? predicting the performance of search-based automatic image classifiers. In Proceedings of the 8th ACM international workshop on Multimedia information retrieval, pages 249--258, New York, NY, USA, 2006. Google ScholarDigital Library
- J. Kustanowitz and B. Shneiderman. Motivating annotation for personal digital photo libraries: Lowering barriers while raising incentives. Technical report, HCIL, Univ. of Maryland, 2004.Google Scholar
- J. Li and J. Z. Wang. Real-time computerized annotation of pictures. In Proceedings of ACM Intl. Conf. on Multimedia, pages 911--920, 2006. Google ScholarDigital Library
- H. Lieberman, E. Rozenweig, and P .Singh. Aria: An agent for annotating and retrieving images. Computer, 34:57--62, 2001. Google ScholarDigital Library
- W.-H. Lin and A. G. Hauptmann. Which thousand words are worth a picture? experiments on video retrieval using a thousand concepts. In Proceedings of IEEE International Conference On Multimedia and Expo (ICME), 2006.Google ScholarCross Ref
- M. Naphade, J. R. Smith, J. Tesic, S.-F. Chang, W. Hsu, L. Kennedy, A. Hauptmann, and J. Curtis. Large-scale concept ontology for multimedia. IEEE MultiMedia, 13(3):86--91, 2006. Google ScholarDigital Library
- P. Over, T. Ianeva, W. Kraaij, and A. F. Smeaton. Trecvid 2006 overview. In {NIST} TRECVID-2006, 2006.Google Scholar
- T. Volkmer, J. R. Smith, and A. Natsev. A web-based system for collaborative annotation of large image and video collections: an evaluation and user study. In Proceedings of the 13th ACM international conference on Multimedia, 2005. Google ScholarDigital Library
- L. von Ahn and L. Dabbish. Labeling images with a computer game. In Proceedings of the SIGCHI conference on Human Factors in computing systems, 2004. Google ScholarDigital Library
- X.-J. Wang, L. Zhang, F. Jing, and W.-Y. Ma. Annosearch: Image auto-annotation by search. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1483--1490, Washington, DC, USA, 2006. IEEE Computer Society. Google ScholarDigital Library
- L. Wenyin, S. Dumais, Y. Sun, H. Zhang, M. Czerwinski, and B. Field. Semi-automatic image annotation. In Interact: Conference on HCI, 2001.Google Scholar
- A. Wilhelm, Y. Takhteyev, R. Sarvas, N. V. House, and M. Davis. Photo annotation on a camera phone. In CHI '04 extended abstracts on Human factors in computing systems, pages 1403--1406, 2004. Google ScholarDigital Library
- Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. In Proc. of the 14th ICML, pages 412--420, 1997. Google ScholarDigital Library
Index Terms
- An efficient manual image annotation approach based on tagging and browsing
Recommendations
Hybrid Tagging and Browsing Approaches for Efficient Manual Image Annotation
This article proposes formal models for two commonly used methods—tagging and browsing—and investigates new approaches to improve the efficiency of manual image annotation.
MAP-based image tag recommendation using a visual folksonomy
Descriptive tags are needed to enable efficient and effective search in vast collections of images. Tag recommendation represents a trade-off between automatic image annotation techniques and manual tagging. In this letter, we formulate image tag ...
Image annotation with tagprop on the MIRFLICKR set
MIR '10: Proceedings of the international conference on Multimedia information retrievalImage annotation is an important computer vision problem where the goal is to determine the relevance of annotation terms for images. Image annotation has two main applications: (i) proposing a list of relevant terms to users that want to assign ...
Comments