Jing Huang
- 1.I. J. Cox, M. L. Miller, S. M. Omohundro, and E N. Yianilos. PicHunter:. Bayesian relevance feedback for image retrieval. Intl. Conf. on Pattern Recognition, 1996. Google Scholar
Digital Library
- 2.R.L.Delanoy. Supervised learning of tools for contentbased search of image databases. SPIE proceedings, 2670:194-205,1996.Google ScholarCross Ref
- 3.M. Flickner, H. S. Sawhney, J. Ashley, Q. Huang, B. Doah M. Gorkani, J .Hafner, D .Lee, D .Petkovie, D. Steele, and P .Yanker. Query by image and video content: The QBIC system. IEEE Computer, 28(9):23- 32, September 1995. Google ScholarDigital Library
- 4.R.M. Haralick. Statistical and structural approaches to texture. Proceedings oflEEE, 67(5):786-804, 1979.Google ScholarCross Ref
- 5.J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabih. Image indexing using color correlograms. Proc. Computer Hsion and Pattern Recognition, pages 762-768, 1997. Google ScholarDigital Library
- 6.$. Huang, S. 1L Kumar, M. Mi'tm, W. J. Zhu. Spatial color indexing and applications. Intl. Conf. on Computer Hsion, 1998. To appear. Google ScholarDigital Library
- 7.T. Minka and IL Picard. interactive learning using a "societyofmodels".Proc. Computer ~sion and Pattern Recognition, 1996. Google ScholarDigital Library
- 8.V. Ogle and M. Stonebraker. Chabot: Retrieval from a relational database of images. IEEE Computer, 28(9):40-48, September 1995. Google ScholarDigital Library
- 9.G. Pass, R. Zabih and J. Miller. Comparing Images Using Color Coherence Vectors. Proceedings ofthe Fourth /I CM Multimedia Conference, pages 65--73, 1996. Google ScholarDigital Library
- 10.G. Pass and R. Zabih. Histogram refinement for content. based image retrieval. IEEE Workshop on Applications ofComputer Hsion, pages 96-102, 1996. Google ScholarDigital Library
- 11.A.?enfland, IL ?icard, and S. Sclaroff. Photobook: Content-based manipulation of imago databases. Intl. Journal of Computer Vision, 180):233-254, 1996. Google ScholarDigital Library
- 12.?. J. Rousseeuw and A. M. Leroy. Robust Regression and Outlier Detection. John Wiley & Sons, 1987. Google ScholarDigital Library
- 13.J. Smith and S-F. Chang. VisualSEEK: a fully automated content-based imago query system. Proceedings of the Fourth A CM Multimedia Conjkrence, pages 87- 98, 1996. Google ScholarDigital Library
- 14.J. Smith and S-F. Chang. Tools and techniques for color image retrieval. SPIE proceedings, 2670:1630--1639, 1996.Google Scholar
- 15.M. Stricker and A. Dimai. Color indexing with weak spatial constraints. $PIE proceedings, 2670:29--40, 1996.Google Scholar
- 16.M. Swain and D. BaUard. Color indexing. Intl. Journal of Computer Hsion, 7(1):11-32, 1991. Google ScholarDigital Library
- 17.G.J.G. Upton and B. Fingleton. Spatial Data Analysts , by Example. VolL John Wiley & Sons, 1985.Google Scholar
Index Terms
- Combining supervised learning with color correlograms for content-based image retrieval
Recommendations
Temporal Color Correlograms for Video Retrieval
ICPR '02: Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1This paper presents a novel method to retrieve segmented video shots based on thier color content. The Temporal Color Correlogram captures the spatio-temporal relationship of colors in a video shot using co-occurrence statistics. The Temporal Color ...
Spatial Color Histograms for Content-Based Image Retrieval
ICTAI '99: Proceedings of the 11th IEEE International Conference on Tools with Artificial IntelligenceColor histogram is an important technique for color image database indexing and retrieving. In this paper, traditional color histogram is modified to capture spatial layout information of each color and three types of spatial color histograms are ...
Content-based image retrieval using joint correlograms
AbstractThe comparison of digital images to determine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis ...
Comments