Image Segmentation Using the K-means Algorithm for Texture Features
This study aims to segment objects using the K-means
algorithm for texture features. Firstly, the algorithm transforms color
images into gray images. This paper describes a novel technique for
the extraction of texture features in an image. Then, in a group of
similar features, objects and backgrounds are differentiated by using
the K-means algorithm. Finally, this paper proposes a new object
segmentation algorithm using the morphological technique. The
experiments described include the segmentation of single and multiple
objects featured in this paper. The region of an object can be
accurately segmented out. The results can help to perform image
retrieval and analyze features of an object, as are shown in this paper.
[1] J. F. Canny, "A Computational Approach to Edge Detection," IEEE
Transaction on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6,
1986, pp. 679-698.
[2] L. Ding and A. Goshtasby, "On the Canny Edge Detector," Pattern
Recognition, Vol. 34, 2001, pp. 721-725.
[3] Rafael C. Gonzalez, and Richard E. Woods, "Digital Image Processing",
Prentice-Hall, 2002.
[4] Pellegrino, F.A. Vanzella, W. Torre, and V., "Edge Detection Revisited,"
IEEE transactions on systems, man, and cybernetics-part B:
CYBERNETICS, Vol. 34, NO. 3, 2004, pp.1500-1518.
[5] Z. Hou, Q. Hu and W. L. Nowinski, "On minimum variance
thresholding," Pattern Recognition Letters, Vol. 27, 2006, pp.
1732-1743.
[6] F. Y. Shih and S. Cheng, "Automatic seeded region growing for color
image segmentation," Image and Vision Computing, Vol. 23, 2005, pp.
877-886.
[7] Mmford, D. and Shah, J., "Optimal approximations by piecewise smooth
function and associated variational problems," Commun.Pure Appl.
Math.42, 1898, pp.577-684.
[8] H. Tamura, S. Mori, and T. Yamawaki, "Texture features corresponding
to visual perception," IEEE Transactions on Systems, Man, and
Cybernetics, Vol. 8, 1978, pp. 460-473.
[9] H. C. Lin, C. Y. Chiu, and S. N. Yang, "Finding textures by textual
descriptions, visual examples, and relevance feedbacks," Pattern
Recognition Letters, vol. 24, 2003, pp. 2255-2267.
[1] J. F. Canny, "A Computational Approach to Edge Detection," IEEE
Transaction on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6,
1986, pp. 679-698.
[2] L. Ding and A. Goshtasby, "On the Canny Edge Detector," Pattern
Recognition, Vol. 34, 2001, pp. 721-725.
[3] Rafael C. Gonzalez, and Richard E. Woods, "Digital Image Processing",
Prentice-Hall, 2002.
[4] Pellegrino, F.A. Vanzella, W. Torre, and V., "Edge Detection Revisited,"
IEEE transactions on systems, man, and cybernetics-part B:
CYBERNETICS, Vol. 34, NO. 3, 2004, pp.1500-1518.
[5] Z. Hou, Q. Hu and W. L. Nowinski, "On minimum variance
thresholding," Pattern Recognition Letters, Vol. 27, 2006, pp.
1732-1743.
[6] F. Y. Shih and S. Cheng, "Automatic seeded region growing for color
image segmentation," Image and Vision Computing, Vol. 23, 2005, pp.
877-886.
[7] Mmford, D. and Shah, J., "Optimal approximations by piecewise smooth
function and associated variational problems," Commun.Pure Appl.
Math.42, 1898, pp.577-684.
[8] H. Tamura, S. Mori, and T. Yamawaki, "Texture features corresponding
to visual perception," IEEE Transactions on Systems, Man, and
Cybernetics, Vol. 8, 1978, pp. 460-473.
[9] H. C. Lin, C. Y. Chiu, and S. N. Yang, "Finding textures by textual
descriptions, visual examples, and relevance feedbacks," Pattern
Recognition Letters, vol. 24, 2003, pp. 2255-2267.
@article{"International Journal of Information, Control and Computer Sciences:57793", author = "Wan-Ting Lin and Chuen-Horng Lin and Tsung-Ho Wu and Yung-Kuan Chan", title = "Image Segmentation Using the K-means Algorithm for Texture Features", abstract = "This study aims to segment objects using the K-means
algorithm for texture features. Firstly, the algorithm transforms color
images into gray images. This paper describes a novel technique for
the extraction of texture features in an image. Then, in a group of
similar features, objects and backgrounds are differentiated by using
the K-means algorithm. Finally, this paper proposes a new object
segmentation algorithm using the morphological technique. The
experiments described include the segmentation of single and multiple
objects featured in this paper. The region of an object can be
accurately segmented out. The results can help to perform image
retrieval and analyze features of an object, as are shown in this paper.", keywords = "k-mean, multiple objects, segmentation, texturefeatures.", volume = "4", number = "5", pages = "955-4", }