Authors A.S. Melnichenko
Month, Year 08, 2009 @en
Index UDC 004.932.72'1
Abstract The problem of automated image annotation is considered in this work. The task is introduced and solved under the assumption of further application of its results to the problem of retrieval large collections of images. Existing methods are reviewed and analyzed their advantages and disadvantages. The task is splitted into stages and the most effective method through the existing and improved solutions is proposed for each stage. The program implementation have been done for all major stages of the concidered methods of image annotation.

Download PDF

Keywords Аutomated image annotation; image retrieval; image processing; image features; probability models; language models; language smoothing.
References 1. Mori Y. Image-to-word transformation based on dividing and vector quantizing images with words / Y. Mori and H. Takahashi and R. Oka // CMU CS Technical Report . 1999.
2. Duygulu P. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary / P. Duygulu, K Barnard, N. de Fretias, D. Forsyth // Proceedings of the European Conference on Computer Vision .– 2002. – P. 97-112.
3. Jeon J. Automatic Image Annotation and Retrieval using Cross-Media Relevance Models. / J. Jeon, V. Lavrenko, R. Manmatha // International Conference of SIGIR - 2003. – P. 679-714.
4. Lavrenko V. A model for learning the semantics of pictures / V. Lavrenko, R. Manmatha, J. Jeon // Proceedings of the 16th Conference on Advances in Neural Information Processing Systems NIPS .– 2003. – P. 85-92.
5. Alexei Yavlinsky. Image indexing and retrieval using automated annotation // PhD thesis, University of London, Imperial College of Science, Technology and Medicine; Department of Computing, 2007.
6. Li J. Real-time Computerized Annotation of Pictures / Jia Li, James Z. Wang // IEEE Transactions on Pattern Analysis and Machine Intelligence – 2008. Vol. 30(6). – P. 985–1002.
7. Oliva A. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope / Aude Oliva, Antonio Torralba // International Journal of Computer Vision. – 2001. Vol. 42. – P. 145-175.
8. Hancock P. The principal components of natural images / P.J. Hancock, R.J. Baddeley, L.S. Smith // Network. – 1992. Vol. 3. – P. 61–70.
9. Henderson J. High level scene perception / J.M. Henderson, A. Hollingworth // Annual Review of Psychology. – 1999. Vol. 50. – P. 243–271.
10. Manning C. Introduction to Information Retrieval / Christopher D. Manning, Prabhakar Raghavan, Hinrich Schьtze // Cambridge University Press. – 2008, 525 p.
11. Абрамов C. Мера содержания фона на основе энтропии для поиска и сортировки изображений в базах данных / С.К. Абрамов, В.В. Лукин, Н.Н. Пономаренко // Радиоэлектронные и компьютерные системы. – 2007. Vol. 2(21). – С. 24-28.
12. Пономаренко H. Устойчивый поиск изображений по полному и тематическому подобию с использованием многопараметровой классификации / Н.Н. Пономаренко and В.В. Лукин and С.К. Абрамов // Интернет-математика – 2007. – С. 171-180.
13. Rao A. Identifying high level features of texture perception / A.R. Rao, G.L. Lohse // Graphical Models and Image Processing – 1993. Vol. 55. – P. 218–233.
14. Tamura H. Texture features corresponding to visual perception / Hideyuki Tamura, Shunji Mori, Takashi Yamawaki // IEEE Trans. On Sys. Man, and Cyb. Vol. 8(6). – P. 460–473.
15. Navneet Dalal. Finding people in images and videos // PhD thesis, Institut National Polytechnique de Grenoble. 2006.
16. Dalal N. Histograms of Oriented Gradients for Human Detection / Navneet Dalal, Bill Triggs // International Conference on Computer Vision & Pattern Recognition – 2005. – P. 886-893.
17. Goncharov A. Pseudometric Approach to Content Based Image Retrieval and Near Duplicates Detection / A. Goncharov, A. Melnichenko // Труды Российского семинара по Оценке Методов Информационного Поиска. – 2008. – P. 120-135.
18. Melnichenko A.S. Content-Based Search of Visually Similar Images using Wavelet-Transformation // Тезисы докладов 9-й Международной научной конференции «Распознавание Образов и анализ изображений: новые информационные технологии» - 2008. – С. 26-29.
19. Zhai C. The Dual Role of Smoothing in the Language Modeling Approach / Chengxiang Zhai, John Lafferty // Proceedings of the Workshop on Language Models for Information Retrieval – 2001. – P. 31-36.

Comments are closed.