Unified JPEG and JPEG-2000 color descriptor for content-based image retrieval

• Autorzy: Forczmański P.
• Języki publikacji: EN

Abstrakty

EN

The problem investigated in this paper refers to image retrieval based on its compressed form, hence giving much advantages in comparison to traditional methods involving image decompression. The main goal of this paper is to discuss a unified visual descriptor for images stored in the two most popular image formats – JPEG/JFIF and JPEG-2000 in the aspect of content-based image retrieval (CBIR). Since the problem of CBIR takes a special interest nowadays, it is clear that new approaches should be discussed. To achieve such goal a unified descriptor is proposed based on low-level visual features. The algorithm operates in both DCT and DWT compressed domains to build a uniform, format-independent index. It is represented by a three-dimensional color histogram computed in CIE L*a*b* color space. Sample software implementation employs a compact descriptor calculated for each image and stored in a database-like structure. For a particular query image, a comparison in the feature-space is performed, giving information about images' similarity. Finally, images with the highest scores are retrieved and presented to the user. The paper provides an analysis of this approach as well as the initial results of application in the field of CBIR.

Słowa kluczowe

EN

JPEG; JPEG 2000; visual descriptor; content-based image retrieval (CBIR)

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Metody Informatyki Stosowanej
• Rocznik: 2008
• Tom: nr 4 (Tom 17)
• Strony: 53--62
• Opis fizyczny: Bibliogr. 16 poz., rys.

Twórcy

autor

Forczmański P.

• Szczecin University of Technology, Faculty of Computer Science and Information Technology

Bibliografia

• [1] Flickner M., Sawhney H., Niblack W., Ashley J., Qian Huang, Dom B., Gorkani M., Hafner J., Lee D., Petkovic D., Steele D., Yanker P. Query by image and video content: the QBIC system. Computer. Vol. 28(9), Sep 1995
• [2] Pentland A., Picard R. W., Sclaroff S. Photobook: Content-based manipulation of image databases. SPIE Storage and Retrieval for Image and Video Databases II, 1994
• [3] Smith J. R., Chang Shih-Fu. VisualSEEk: a Fully Automated Content-Based Image Query System. ACM Multimedia, Boston, MA, November 1996.
• [4] Forczmański P., Frejlichowski D. Strategies of shape and color fusions for content based image retrival, Computer Recognition System 2, Springer, Berlin, 2007
• [5] Kukharev G., Mikłasz M.: Face Retrieval from Large Database, Polish Journal of Environmental Studies, vol. 15, no. 4C, 2006
• [6] Deng Y., Manjunath B. S., Kenney C., Moore M. S., Shin H.: An Efficient Color Representation for Image Retrieval. IEEE Transactions on Image Processing, vol. 10, no.1, 2001
• [7] Manjunath B. S., Ohm J.-R., Vasudevan V. V., Yamada A.: Color and Texture Descriptors. IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 6, 2001
• [8] Guocan Feng, Jianmin Jiang. Jpeg compressed image retrieval via statistical features. Pattern Recognition, 36(4):977–985, April 2003
• [9] Au K.M. , Law N.F. , Siu W.C.. Unified feature analysis in jpeg and jpeg 2000-compressed domains. Pattern Recognition, 40(7):2049–2062, 2007
• [10] Forczmański P., Bania A. Content-based Image Retrieval in Compressed Domain. Polish Journal of Environmental Studies, 2008
• [11] Fairchild M. D. Color Appearance Models. John Wiley and Sons Ltd., 2005
• [12] Lab color space, http://en.wikipedia.org/wiki/Lab_color_space [online]
• [13] Lu G., Phillips J. Using perceptually weighted histograms for colour-based image retrieval. Fourth International Conference on Signal Processing, Beijing, 1998
• [14] Schaefer G. Jpeg image retrieval by simple operators. Proceedings of the International Workshop on Content-Based Multimedia Indexing, Brescia, Italy, 2001
• [15] Free Public Domain Photo Database: pdphoto.org [downloaded 08.05.2008]
• [16] Free Images – Free Stock Photos: www.freeimages.co.uk [downloaded 09.05.2008]