Comparison of several approaches for the segmentation of texture images

Zhiling Wang; Andrea Guerriero; Marco De Sario

doi:10.1117/12.205259

28 March 1995 Comparison of several approaches for the segmentation of texture images

Zhiling Wang, Andrea Guerriero, Marco De Sario

Proceedings Volume 2424, Nonlinear Image Processing VI; (1995) https://doi.org/10.1117/12.205259
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

In this paper, several approaches including K-means, fuzzy K-means (FKM), fuzzy adaptive resonance theory (ART2) and fuzzy Kohonen self-organizing feature mapping (SOFM) are adapted to segment the texture image. In our tests five features, energy, entropy, correlation, homogeneity, and inertia, are used in texture analysis. The K-means algorithm has the following disadvantages: (1) supervised learning mode, (2) slow real-time ability, (3) instability. The FKM algorithm has improved the performance of the instability by means of the introduction of fuzzy distribution functions. The fuzzy ART2 has advantages, such as unsupervised training, high computation rates, and a great degree of fault tolerance (stability/plasticity). Fuzzy operator and mapping functions are added in the network to improve the generality. The fuzzy SOFM integrates the FKM algorithm into fuzzy membership value as a learning rate and updates stratifies of the Kohonen network. This yields automatic adjustment of both the learning rate distribution and update neighborhood, and has an optimization problem related to FKM. Therefore, the fuzzy SOFM is independent of the sequence of feed of input patterns whereas final weight vectors by the Kohonen method depend on the sequence. The fuzzy SOFM is `self-organizing' since the `size' of the update neighborhood and learning rate are automatically adjusted during learning. Clustering errors are reduced by fuzzy SOFM as well as better convergence. The numerical results show that fuzzy ART2 and fuzzy SOFM are better than K-means algorithms. The images segmented by the algorithms are given to prove their performances.

Citation Download Citation

Zhiling Wang, Andrea Guerriero, and Marco De Sario "Comparison of several approaches for the segmentation of texture images", Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); https://doi.org/10.1117/12.205259

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available