Texture segmentation using fractal dimension
Chaudhuri, B.B.
Sarkar, N.
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jan 1995
Volume: 17,
Issue: 1
On page(s): 72-77
ISSN: 0162-8828
References Cited: 30
CODEN: ITPIDJ
INSPEC Accession Number: 4871856
Digital Object Identifier: 10.1109/34.368149
Current Version Published: 2002-08-06
Abstract
This paper deals with the problem of recognizing and segmenting
textures in images. For this purpose the authors employ a technique
based on the fractal dimension (FD) and the multi-fractal concept. Six
FD features are based on the original image, the above average/high gray
level image, the below average/low gray level image, the horizontally
smoothed image, the vertically smoothed image, and the multi-fractal
dimension of order two. A modified box-counting approach is proposed to
estimate the FD, in combination with feature smoothing in order to
reduce spurious regions. To segment a scene into the desired number of
classes, an unsupervised K-means like clustering approach is used.
Mosaics of various natural textures from the Brodatz album as well as
microphotographs of thin sections of natural rocks are considered, and
the segmentation results to show the efficiency of the technique.
Supervised techniques such as minimum-distance and k-nearest neighbor
classification are also considered. The results are compared with other
techniques
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