Abstract
In this paper the combination of texture and colour features is used for image classification. Texture features are extracted using the Discrete Wavelet Frame analysis. 2-D or 1-D histograms of the CIE Lab chromaticity coordinates are used as colour features. The 1-D histograms of the a, b coordinates were also modeled according to the generalized Gaussian distribution. The similarity measure defined on the features distribution is based on the Bhattacharya distance. Retrieval benchmarking is performed on textured colour images from natural scenes, obtained from the VisTex database of MIT Media Laboratory and from the Corel Photo Gallery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M. Flickner, H. Sawhney, W. Niblack, and J Ashley. Query by image and video content: the qbic system. IEEE Computer, 28(9):23–32, Sep 1995.
V. N. Gudivada and V. V. Raghavan. Content based image retrieval systems. IEEE Computer, 28, Sep 1995.
A. Gupta and R. Jain. Visual information retrieval. Communicastions of the A.C.M., 40(5):70–79, May 1997.
MIT Media Laboratory. Vistex: Texture image database. http://www-white.media.mit.edu/vismod/imagery/VisionTexture/vistex.html.
S. Liapis, N. Alvertos, and G. Tziritas. Maximum likelihood texture classification and bayesian texture segmentation using discrete wavelet frames. Intern. Conf. in Digital Signal Processing DSP97, 2:1107–1110, July 1997.
F. Liu and R. W. Picard. Periodicity directionality and randomness wold features for image modeling retrieval. Pattern Recognition, 18(7):722–733, Jul 1996.
S. G. Malat. A theory of multiresolution signal decomposition: The wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 11:674–693, January 1989.
V. Ogle and M. Stonebraker. Chabot: Retrieval from a relational database of images. IEEE Computer, 28(9):40–48, Sep 1995.
A. Pentland, R. W. Picard, and S. Sclaroff. Photobook: Content-based manipulation of image databases. M.I.T. Media Laboratory Perceptual Computing Technical Report No. 255, November 1993.
J. Puzicha, J. Buhmann, Y. Rubner, and C. Tomasi. Empirical evaluation of dissimilarity measures for color and texture. Intern. Conf. on Computer Vision, Sep 1999.
M. Unser. Texture classification and segmentation using wavelet frames. IEEE Trans. on Image Processing, 4:1549–1560, November 1995.
T. Young and K.S. Fu. Handbook of pattern recognition and image processing. Academic Press, 1986.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liapis, S., Tziritas, G. (2000). Image Retrieval by Colour and Texture Using Chromaticity Histograms and Wavelet Frames. In: Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2000. Lecture Notes in Computer Science, vol 1929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40053-2_35
Download citation
DOI: https://doi.org/10.1007/3-540-40053-2_35
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41177-2
Online ISBN: 978-3-540-40053-0
eBook Packages: Springer Book Archive