Text and picture segmentation by the distribution analysis of wavelet coefficients

This paper presents an algorithm to segment text and picture in an image using two features based on the statistical distribution of the wavelet coefficients in high frequency bands. The algorithm breaks the image into blocks and classifies every block as background, text or picture according to the two features. The block size is variable so that the segmentation can be accurate at the boundary of two types and avoids misclassifying due to over-localized region analysis.

[1]  Dennis M. Healy,et al.  Classification of masses in digitized mammograms with features in the wavelet transform domain , 1994, Defense, Security, and Sensing.

[2]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[3]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[4]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[5]  G. W. Snedecor.  Statistical Methods , 1964 .

[6]  Atam P. Dhawan,et al.  Analysis of mammographic microcalcifications using gray-level image structure features , 1996, IEEE Trans. Medical Imaging.

[7]  J. Lemoine,et al.  A new method for texture classification based on wavelet transform , 1996, Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96).

[8]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[9]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.