An Algorithm for Measuring the Similarity of Histograms for Texture Image Segmentation-Reference-Cited by-同舟云学术

An Algorithm for Measuring the Similarity of Histograms for Texture Image Segmentation

Published:2024-02-20 Issue: Volume:21 Page:107-115
ISSN:2224-3402
Container-title:WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
language:en
Short-container-title:

Author:

Goltsev Alexander¹,Holtsev Oleksii²

Affiliation:

1. Department of Neural Information Processing Technologies, International Research and Training Centre for Information Technologies & Systems of the NAS & MES, Acad. Glushkov ave., 40, Kiev, 03187, UKRAINE

2. Department of Digital Environmental Monitoring Systems, International Research and Training Centre for Information Technologies & Systems of the NAS & MES, Acad. Glushkov ave., 40, Kiev, 03187, UKRAINE

Abstract

A simple algorithm for measuring the similarity between multi-column histograms is presented. The proposed algorithm is intended for texture segmentation of images using histograms as texture features. The purpose of developing such a specialized algorithm is to more accurately determine the boundaries between neighboring texture segments. The algorithm is specially designed so that to express the similarity value as a percentage. The main peculiarity of the proposed algorithm is that when calculating the similarity value, it considers not only the corresponding histogram columns but also takes into account their neighboring components. Due to this, the algorithm more adequately evaluates the similarity of histograms. The proposed algorithm was implemented as a computer program as an integral part of the image segmentation model. The efficiency of the histogram comparison algorithm is indirectly confirmed by the texture segmentation results of the image segmentation model in image processing experiments.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Reference29 articles.

1. E. M. Kussul, D. A. Rachkovskij, T. N. Baidyk, On image texture recognition by associative-projective neurocomputer, Proceedings of the Intelligent Engineering Systems through Artificial Neural Networks (ANNIE’91), St. Louis, Missouri, U.S.A., 1991, pp. 453–458.

2. E.M. Kussul, T.N. Baidyk, V.V. Lukovitch, D.A. Rachkovskij, Adaptive neural network classifier with multifloat input coding, Proceedings of the 6th International Conference “Neuro-Nimes 93”, Nimes, 1993, pp. 209–216.

3. J. Ruiz-del-Solar, Neural-based architectures for the segmentation of textures, Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain: 2000, Vol. 3, pp. 1080–1083, http://dx.doi.org/10.1109/ICPR.2000.903733.

4. D.R. Martin, C.C. Fowlkes, J. Malik, Learning to detect natural image boundaries using local brightness, colour, and texture cues, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 26, No. 5, 2004, pp. 530-549.

5. F.H.C. Tivive, A. Bouzerdoum, Texture classification using convolutional neural networks, Proceedings of IEEE Region 10 Conference, Hong Kong, China, 2006, pp. 1– 4, http://dx.doi.org/10.1109/TENCON.2006.343 944.