Implicit visual concept modeling in image / video annotation
Pages 33 - 38
Abstract
In this paper a novel approach for automatically annotating image databases is proposed. Despite most current approaches that are just based on spatial content analysis, the proposed method properly combines implicit feedback information and visual concept models for semantically annotating images. Our method can be easily adopted by any multimedia search engine, providing an intelligent way to even annotate completely non-annotated content. The proposed approach currently provides very interesting results in limited-content environments and it is expected to add significant value to billions of non-annotated images existing in the Web. Furthermore expert annotators can gain important knowledge relevant to user new trends, language idioms and styles of searching.
References
[1]
J. Nesvadba, "From push-based passive content consumption to pull-based content experiences," Panel presentation in the 8th IEEE WIAMIS, Santorini, Greece, 2007.
[2]
V.S. Tseng, J.-H. Su, J.-H. Huang, and C.-J. Chen, "Integrated Mining of Visual Features, Speech Features, and Frequent Patterns for Semantic Video Annotation," IEEE Transactions on Multimedia, vol. 10, no. 2, pp. 260--267, February 2008.
[3]
K. Petridis, I. Kompatsiaris, M. G. Strintzis, S. Bloehdorn, S. Handschuh, S. Staab, N. Simou, V. Tzouvaras, and Y. Avrithis, "Knowledge Representation for Semantic Multimedia Content Analysis and Reasoning," Proceedings of the 2004 European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology, pp. 33--46, London, UK, November 2004.
[4]
J. Assfalg, M. Bertini, C. Colombo, and A.D. Bimbo, "Semantic annotation of sports videos," IEEE Multimedia, vol. 9, no. 2, pp. 52--60, April-June 2002.
[5]
N. Tsapatsoulis, and S. Petridis, "Classifying Images from Athletics Based on Spatial Relations," Proceedings of the 2nd Intern. Workshop on Semantic Media Adaptation and Personalization, pp. 92--97, December 2007.
[6]
K. Rapantzikos, N. Tsapatsoulis, Y. Avrithis, and S. Kollias, "Bottom-up spatiotemporal visual attention model for video analysis," IET Image Processing, vol. 1, no. 2, pp.237 -- 248, June 2007.
[7]
S. Gao, D.-H. Wang, and C.-H. Lee, "Automatic Image Annotation through Multi-Topic Text Categorization," Proceedings of the 2006 IEEE ICASSP, vol. 2, pp. II--II, Toulouse, France, May 2006.
[8]
N. Doulamis and A. Doulamis, "Evaluation of Relevance Feedback Schemes in Content-based Retrieval Systems," Signal Processing: Image Communication, Vol. 21, No 4, pp. 334--357, April 2006.
[9]
H. Muller, T. Pun, and D. Squire, "Learning from user behavior in image retrieval: Application of market basket analysis," International Journal of Computer Vision, Vol. 56, No. 1--2, p.p. 65--77, 2004.
[10]
A. Natsev, W. Jiang, M. Merler, J. R. Smith, J. Tesic, L. Xie, and R. Yan, "IBM Research TRECVID-2008 Video Retrieval System," In Proc. of TRECVID 2008, 2008.
[11]
A. Ulges, M. Koch, C. Schulze, and T. Breuel, "Learning TRECVID'08 high-level features from YouTubeTM," In Proc. of TRECVID 2008, 2008.
[12]
X.-J. Wang, W.-Y. M. Ma, and X. Li, "Exploring statistical correlations for image retrieval," Multimedia Systems, Vol. 11, No. 4, p.p. 340--351, 2006.
[13]
T. Joachims, L. Granka, B. Pang, H. Hembrooke, and G. Gay, "Accurately interpreting clickthrough data as implicit feedback," In Proceedings of the 28th Annual International ACM SIGIR Conference, p.p. 154--161, 2005.
[14]
C. Macdonald and I. Ounis, "Usefulness of quality click-through data for training," In Proceedings of the 2009 Workshop on Web Search Click Data, p.p. 75--79, 2009.
[15]
T. Tsikrika, C. Diou, A. P. de Vries, and A. Delopoulos, "Image annotation using clickthrough data, " In Proceedings of the 8th International Conference on Image and Video Retrieval, 2009.
[16]
X. He, O. King, W.-Y. Ma, M. Li, and H.-J. Zhang, "Learning a semantic space from user's relevance feedback for image retrieval," IEEE Trans. on Circuits and Systems for Video Technology, Vol. 13, No. 1, p.p. 39--48, 2003.
[17]
D. Heisterkamp, "Building a latent-semantic index of an image database from patterns of relevance feedback," In Proceedings of the 16th ICPR, p.p. 134--137, 2002.
[18]
C. Lin, G.-R. Xue, H.-J. Zeng, and Y. Yu, "Using probabilistic latent semantic analysis for personalized web search," In Web Technologies Research and Development - Proceedings of the 7th Asia-Pacific Web Conference, Vol. 3399 of LNCS, p.p. 707--717, 2005.
[19]
K. S. Ntalianis, N. Tsapatsoulis, A. Doulamis and N. Doulamis, "Event Recognition in Video Streams based on User Cognition," in Proc. of the 1st ACM Intel Workshop in Analysis and Retrieval of Events/Actions and Workflows in Video Streams, Vancouver, Canada, October 2008.
[20]
K. Ntalianis, A. Doulamis, N. Tsapatsoulis and N. Doulamis, "Human Action Analysis, Annotation and Modeling in Video Streams Based on Implicit User Interaction," in Multimedia Tools and Applications, Springer, accepted for publication, 2009.
[21]
A. Doulamis, N. Doulamis, and S. Kollias, "Non-sequential video content representation using temporal variation of feature vectors," IEEE Transactions on Consumer Electronics, vol. 46, no. 3, pp. 758--768, August 2000.
[22]
R. T. Ng and J. Han, "CLARANS: a method for clustering objects for spatial data mining," IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 5, pp. 1003--1016, 2002.
Index Terms
- Implicit visual concept modeling in image / video annotation
Recommendations
Automatic annotation of image databases based on implicit crowdsourcing, visual concept modeling and evolution
In this paper a novel approach for automatically annotating image databases is proposed. Despite most current schemes that are just based on spatial content analysis, the proposed method properly combines several innovative modules for semantically ...
Image annotation by large-scale content-based image retrieval
MM '06: Proceedings of the 14th ACM international conference on MultimediaImage annotation has been an active research topic in recent years due to its potentially large impact on both image understanding and Web image search. In this paper, we target at solving the automatic image annotation problem in a novel search and ...
Scalable search-based image annotation of personal images
MIR '06: Proceedings of the 8th ACM international workshop on Multimedia information retrievalWith the prevalence of digital cameras, more and more people have considerable digital images on their personal devices. As a result, there are increasing needs to effectively search these personal images. Automatic image annotation may serve the goal, ...
Comments
Information & Contributors
Information
Published In
October 2010
104 pages
ISBN:9781450301633
DOI:10.1145/1877868
- General Chairs:
- Anastasios Doulamis,
- Jordi Gonzàlez,
- Program Chairs:
- Marco Bertini,
- Luc van Gool,
- Nikolaos Matsatsinis,
- Thomas B. Moeslund,
- Mark Nixon,
- Liang Wang
Copyright © 2010 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 October 2010
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
MM '10
Sponsor:
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 106Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in