Albrecht Lindner
ABSTRACT
This thesis aims at developing methods to make digital devices more automatic and intuitive while focusing on image-related applications. We learn associations between image characteristics and keywords with a statistical framework based on large databases of annotated images. Such associations are widely exploitable and we demonstrate two main applications: semantic image enhancement and automatic color naming. Both applications show convincing results and suggest that the framework can be extended to other areas.
- XKCD color survey, http://blog.xkcd.com/2010/05/ 03/color-survey-results/, last checked apr. 2012.Google Scholar
- L. Cieplinski. MPEG-7 Color Descriptors and Their Applications. In Computer Analysis of Images and Patterns, volume 2124, pages 11--20, 2001. Google ScholarDigital Library
- D. Cohen-Or, O. Sorkine, R. Gal, T. Leyvand, and Y.-Q. Xu. Color harmonization. ACM Trans. Graphics, 25(3):624--630, 2006. Google ScholarDigital Library
- C. Cusano, F. Gasparini, and R. Schettini. Image annotation for adaptive enhancement of uncalibrated color images. In Lecture Notes in Computer Science, volume 3736, pages 216--225, 2006. Google ScholarDigital Library
- J. Davidoff, I. Davies, and D. Roberson. Colour categories in a stone-age tribe. Nature, 398:203--204, 1999.Google ScholarCross Ref
- J. Frank J. Massey. The Kolmogorov-Smirnov Test for Goodness of Fit. In American Statistical Association, volume 46, pages 68--78, 1951.Google ScholarCross Ref
- M. J. Huiskes and M. S. Lew. The MIR flickr retrieval evaluation. In ACM MIR, 2008. Google ScholarDigital Library
- R. Hummel. Image Enhancement by Histogram Transformation. CGIP, 6(2):184--195, 1977.Google Scholar
- A. Lindner, N. Bonnier, and S. Süsstrunk. A large-scale multi-lingual color thesaurus. In CIC (to appear), 2012.Google Scholar
- A. Lindner, N. Bonnier, and S. Süsstrunk. What is the color of chocolate? - extracting color values of semantic expressions. In CGIV, 2012.Google Scholar
- A. Lindner, A. Shaji, N. Bonnier, and S. Süsstrunk. Joint statistical analysis of images and keywords with applications in semantic image enhancement. ACM MM, 2012. Google ScholarDigital Library
- N. Moroney. Unconstrained web-based color naming experiment. In Color Imaging VIII: Processing, Hardcopy, and Applications, volume 5008, pages 36--46. EI, 2003.Google Scholar
- S. Moser and M. Schroeder. Usage of DSC meta tags in a general automatic image enhancement system. In EI, volume 4669, pages 259--267, San Jose, CA, USA, January 2002.Google Scholar
- D. Mylonas, L. MacDonald, and S. Wuerger. Towards an Online Color Naming Model. In CIC, pages 140--144, 2010.Google Scholar
- R. L. Plackett. Karl pearson and the chi-squared test. International Statistical Review, 51(1):59--72, 1983.Google ScholarCross Ref
- E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley. Color transfer between images. IEEE Computer Graphics and Applications, 21(5):2--9, 2001. Google ScholarDigital Library
- C. Sauvaget and V. Boyer. Harmonic colorization using proportion contrast. In AFRIGRAPH, pages 63--69, 2010. Google ScholarDigital Library
- D. Sekulovski, G. Geleijnse, B. Kater, J. Korst, S. Pauws, and R. Clout. Enriching text with images and colored light. In EI, volume 6820, 2008.Google Scholar
- J. van de Weijer, C. Schmid, J. Verbeek, and D. Larlus. Learning color names for real-world applications. TIP, 18(7):1512--1523, 2009. Google ScholarDigital Library
- F. Wilcoxon. Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80--83, 1945.Google ScholarCross Ref
- I. H. Witten, E. Frank, and M. A. Hall. Data Mining: Practical Machine Learning Tools and Techniques, volume 3. Morgan Kaufmann, 2011. Google ScholarDigital Library
- S. Zhuo and T. Sim. Defocus map estimation from a single image. Pattern Recognition, 44(9):1852--1858, 2011. Google ScholarDigital Library
Index Terms
- Semantic awareness for automatic image interpretation
Recommendations
Semantic context learning with large-scale weakly-labeled image set
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementThere are a large number of images available on the web; meanwhile, only a subset of web images can be labeled by professionals because manual annotation is time-consuming and labor-intensive. Although we can now use the collaborative image tagging ...
An automatic image inpaint tool
MULTIMEDIA '03: Proceedings of the eleventh ACM international conference on MultimediaAutomatic digital inpainting is a challenge but interesting research area. This demonstration presents a tool, which uses a color interpolation mechanism to restore damaged images. The mechanism checks the variation of pixel blocks and restores pixels ...
Automatic image authentication and recovery using fractal code embedding and image inpainting
In this paper, we propose a novel scheme of automatic image authentication and recovery, in which the altered region of the image can be detected and recovered in a fully automated fashion. To achieve high-quality image recovery, we choose the region of ...
Comments