Fast image auto-annotation with discretized feature distance measures

• Autorzy: Kwasnicka H. , Paradowski M.
• Języki publikacji: EN

Abstrakty

EN

A new model for the image auto-annotation task is presented. The model can be classified as a fast image auto-annotation one. The main idea behind the model is to avoid various problems with feature space clustering. Both the image segmentation and the auto-annotation process do not, use any clustering algorithms. The method presented here simulates continuous feature space analysis with very dense discretization. The paper presents the new approach and discusses the results achieved with it.

Słowa kluczowe

EN

image auto-annotation; fast auto-annotation; image feature extraction

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2006
• Tom: Vol. 15, No. 2
• Strony: 123--140
• Opis fizyczny: Bibliogr. 13 poz., il., wykr.

Twórcy

autor

Kwasnicka H.

autor

Paradowski M.

• Institute of Applied Informatics, Wroclaw University of Technology

Bibliografia

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• [2] ECCV 2002 image dataset, http://kobus.ca/research/data/eccv_2002/index.html
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• [5] Lavrenko V., Manmatha R., Jeon J.: A Model for Learning the Semantics of Pictures, In Proc. of NIPS'03, 2003. http://ciir.cs.umass.edu/pubfiles/mm-46.pdf
• [6] Monay F., Gatica-Perez D.: On Image Auto-Annotation with Latent Space Models, In Proc. ACM Int. Conf. on Multimedia (ACM MM), Berkeley, November 2003. http://citeseer.ist.psu.edu/context/2411445/660339
• [7] Pan J., Yang H., Faloutsos C, Duygulu P.: GCap: Graph-based Automatic Image Captioning, In Proceedings of the 4th International Workshop on Multimedia Data and Document Engineering (MDDE 04), in conjunction with Computer Vision Pattern Recognition Conference (CVPR 04), 2004. Washington DC, July 2nd 2004. http://www.cs.cmu.edu/ jypan/publicationsl/b2hd-MDDE04GCap.html
• [8] Feng S. L., Manmatha R. and Lavrenko V.: Multiple Bernoulli Relevance Models for Image and Video Annotation, 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (GVPR'04) - Vol. 2, pp. 1002-1009, 2004
• [9] ICPR 2005 image dataset, http://www.cs.washington.edu/research/imagedatabase/groundtruth/2005
• [10] Glotin H., Tollari S.: Fast Image Auto-Annotation with Visual Vector Approximation Clusters, CBMI 2005, http://sis.univ-tln.fr/tollari/ ARTICLES/CBMI2005/TOLLARI-CBMI2005.pdf
• [11] Ye J., Zhou X., Pei J., Chen L., Zhang L.: A Stratification-Based Approach to Accurate and Fast Image Annotation, In Proceedings of the 6th International Conference on Web-Age Information Management (WAIM'05), Hangzhou, China, October 11-13, 2005. WAIM 2005: 284-296 http://www.cs.sfu.ca/ jpei/publications/annotation-waimO5.pdf
• [12] Virga P., Duygulu P.:Systematic Evaluation of Machine Translation Methods for Image and Video Annotation, In Proceedings of The Fourth International Conference on Image and Video Retrieval (CIVR 2005), Singapore, July 20-22, 2005. Also published in Lecture Notes in Computer Science, Vol. 3568/2005 pp. 174-183. http://www.cs.bilkent.edu.tr/ duygulu/papers/CIVR2005-annotation.pdf
• [13] Kwasnicka H., Paradowski M..: Multiple Class Machine Learning Approach for Image Auto-Annotation Problem, In Proceedings of The Sixth International Conference on Intelligent Systems Design and Applications (ISDA2006), 2006.