A New Approach for Image Segmentation using Pillar-Kmeans Algorithm
This paper presents a new approach for image
segmentation by applying Pillar-Kmeans algorithm. This
segmentation process includes a new mechanism for clustering the
elements of high-resolution images in order to improve precision and
reduce computation time. The system applies K-means clustering to
the image segmentation after optimized by Pillar Algorithm. The
Pillar algorithm considers the pillars- placement which should be
located as far as possible from each other to withstand against the
pressure distribution of a roof, as identical to the number of centroids
amongst the data distribution. This algorithm is able to optimize the
K-means clustering for image segmentation in aspects of precision
and computation time. It designates the initial centroids- positions
by calculating the accumulated distance metric between each data
point and all previous centroids, and then selects data points which
have the maximum distance as new initial centroids. This algorithm
distributes all initial centroids according to the maximum
accumulated distance metric. This paper evaluates the proposed
approach for image segmentation by comparing with K-means and
Gaussian Mixture Model algorithm and involving RGB, HSV, HSL
and CIELAB color spaces. The experimental results clarify the
effectiveness of our approach to improve the segmentation quality in
aspects of precision and computational time.
[1] J.L. Marroquin, F. Girosi, "Some Extensions of the K-Means Algorithm
for Image Segmentation and Pattern Classification", Technical Report,
MIT Artificial Intelligence Laboratory, 1993.
[2] K. Atsushi, N. Masayuki, "K-Means Algorithm Using Texture
Directionality for Natural Image Segmentation", IEICE technical report.
Image engineering, 97 (467), pp.17-22, 1998.
[3] A. Murli, L. D-Amore, V.D. Simone, "The Wiener Filter and
Regularization Methods for Image Restoration Problems", Proc. The
10th International Conference on Image Analysis and Processing, pp.
394-399, 1999.
[4] S. Ray, R.H. Turi, "Determination of number of clusters in K-means
clustering and application in colthe image segmentation", Proc. 4th
ICAPRDT, pp. 137-143, 1999.
[5] T. Adani, H. Ni, B. Wang, "Partial likelihood for estimation of multiclass
posterior probabilities", Proc. the IEEE International Conference
on Acoustics, Speech, and Signal Processing, Vol. 2, pp. 1053-1056,
1999.
[6] B. Kövesi, J.M. Boucher, S. Saoudi, "Stochastic K-means algorithm for
vector quantization", Pattern Recognition Letters, Vol. 22, pp. 603-610,
2001.
[7] J.Z. Wang, J. Li, G. Wiederhold, "Simplicity: Semantics-sensitive
integrated matching for picture libraries", IEEE Transactions on Pattern
Analysis and Machine Intelligence, 23 (9), pp. 947-963, 2001.
[8] Y. Gdalyahu, D. Weinshall, M. Wermen, "Self-Organizationin Vision:
Stochastic clustering for Image Segmentation, Perceptual Grouping, and
Image database Organization", IEEE Transactions on Pattern Analysis
and Machine Intelligence, Vol. 23, No. 12, pp. 1053-1074, 2001.
[9] C. Carson, H. Greenspan, "Blobworld: Image Segmentation Using
Expectation-Maximization and Its Application to Image Querying",
IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol.
24, No. 8, pp. 1026-1038, 2002.
[10] C.J. Veenman, M.J.T. Reinders, E. Backer, "A maximum variance
cluster algorithm", IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 24, No. 9, pp. 1273-1280, 2002.
[11] B. Wei, Y. Liu, Y. Pan, "Using Hybrid Knowledge Engineering and
Image Processing in Color Virtual Restoration of Ancient Murals", IEEE
Transactions on Knowledge and Data Engineering, Vol. 15, No. 5, 2003.
[12] M. Luo, Y.F. Ma, H.J. Zhang, "A Spatial Constrained K-Means
Approach to Image Segmentation", Proc. the 2003 Joint Conference of
the Fourth International Conference on Information, Communications
and Signal Processing and the Fourth Pacific Rim Conference on
Multimedia, Vol. 2, pp. 738-742, 2003.
[13] Y.M. Cheung, "k*-Means: A new generalized k-means clustering
algorithm", Pattern Recognition Letters, Vol. 24, pp. 2883-2893, 2003.
[14] A.R. Barakbah, K. Arai, "Identifying moving variance to make
automatic clustering for normal dataset", Proc. IECI Japan Workshop
(IJW), Tokyo, 2004.
[15] H.M. Lotfy, A.S. Elmaghraby, "CoIRS: Cluster-oriented Image
Retrieval System", Proc. 16th IEEE International Conference on Tools
with Artificial Intelligence (ICTAI), pp. 224-231, 2004.
[16] N.J. Kwak, D.J. Kwon, Y.G. Kim, J.H. Ahn, "Color image segmentation
using edge and adaptive threshold value based on the image
characteristics", Proc. International Symposium on Intelligent Signal
Processing and Communication Systems (ISPACS), pp. 255-258. 2004.
[17] S.S. Khan, A. Ahmad, "Cluster center initialization algorithm for Kmeans
clustering", Pattern Recognition Letters, Vol. 25, pp. 1293-1302,
2004.
[18] A.R. Barakbah, A. Helen, "Optimized K-means: an algorithm of initial
centroids optimization for K-means", Proc. Seminar on Soft Computing,
Intelligent System, and Information Technology (SIIT), Surabaya, 2005.
[19] K.L. Priddy, P.E. Keller, Artificial Neural Networks, pp. 16-17, SPIE
Publications, 2005.
[20] A.M. Us├│, F. Pla, P.G. Sevila, "Unsupervised Image Segmentation
Using a Hierarchical Clustering Selection Process", Structural,
Syntactic, and Statistical Pattern Recognition, Vol. 4109, pp. 799-807,
2006.
[21] A.Z. Arifin, A. Asano, "Image segmentation by histogram thresholding
using hierarchical cluster analysis", Pattern Recognition Letters, Vol. 27,
no. 13, pp. 1515-1521, 2006.
[22] B. Mičušík, A. Hanbury, "Automatic Image Segmentation by
Positioning a Seed*", ECCV 2006, Part II, LNCS 3952, Springer
Berlin/Heidelberg, pp. 468-480, 2006.
[23] J. Chen, J. Benesty, Y.A. Huang, S. Doclo, "New Insights Into the Noise
Reduction Wiener Filter", IEEE Transactions on Audio, Speech, and
Language Processing, Vol. 14, No. 4, 2006.
[24] Y. Pan, J.D. Birdwell, S.M. Djouadi, "Bottom-Up Hierarchical Image
Segmentation Using Region Competition and the Mumford-Shah
Functional", Proc. 18th International Conference on Pattern Recognition
(ICPR), Vol. 2, pp. 117-121, 2006.
[25] L. Jin, D. Li, "A Switching vector median based on the CIELAB color
space for color image restoration", Signal Processing, Vol. 87, pp.1345-
1354, 2007.
[26] A.R. Barakbah, Y. Kiyoki, "A Pillar Algorithm for K-Means
Optimization by Distance Maximization for Initial Centroid
Designation", IEEE Symposium on Computational Intelligence and Data
Mining (CIDM), Nashville-Tennessee, 2009.
[27] A.R. Barakbah, Y. Kiyoki, "An Image Database Retrieval System with
3D Color Vector Quantization and Cluster-based Shape and Structure
Features", The 19th European-Japanese Conference on Information
Modelling and Knowledge Bases, Maribor, 2009.
[1] J.L. Marroquin, F. Girosi, "Some Extensions of the K-Means Algorithm
for Image Segmentation and Pattern Classification", Technical Report,
MIT Artificial Intelligence Laboratory, 1993.
[2] K. Atsushi, N. Masayuki, "K-Means Algorithm Using Texture
Directionality for Natural Image Segmentation", IEICE technical report.
Image engineering, 97 (467), pp.17-22, 1998.
[3] A. Murli, L. D-Amore, V.D. Simone, "The Wiener Filter and
Regularization Methods for Image Restoration Problems", Proc. The
10th International Conference on Image Analysis and Processing, pp.
394-399, 1999.
[4] S. Ray, R.H. Turi, "Determination of number of clusters in K-means
clustering and application in colthe image segmentation", Proc. 4th
ICAPRDT, pp. 137-143, 1999.
[5] T. Adani, H. Ni, B. Wang, "Partial likelihood for estimation of multiclass
posterior probabilities", Proc. the IEEE International Conference
on Acoustics, Speech, and Signal Processing, Vol. 2, pp. 1053-1056,
1999.
[6] B. Kövesi, J.M. Boucher, S. Saoudi, "Stochastic K-means algorithm for
vector quantization", Pattern Recognition Letters, Vol. 22, pp. 603-610,
2001.
[7] J.Z. Wang, J. Li, G. Wiederhold, "Simplicity: Semantics-sensitive
integrated matching for picture libraries", IEEE Transactions on Pattern
Analysis and Machine Intelligence, 23 (9), pp. 947-963, 2001.
[8] Y. Gdalyahu, D. Weinshall, M. Wermen, "Self-Organizationin Vision:
Stochastic clustering for Image Segmentation, Perceptual Grouping, and
Image database Organization", IEEE Transactions on Pattern Analysis
and Machine Intelligence, Vol. 23, No. 12, pp. 1053-1074, 2001.
[9] C. Carson, H. Greenspan, "Blobworld: Image Segmentation Using
Expectation-Maximization and Its Application to Image Querying",
IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol.
24, No. 8, pp. 1026-1038, 2002.
[10] C.J. Veenman, M.J.T. Reinders, E. Backer, "A maximum variance
cluster algorithm", IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 24, No. 9, pp. 1273-1280, 2002.
[11] B. Wei, Y. Liu, Y. Pan, "Using Hybrid Knowledge Engineering and
Image Processing in Color Virtual Restoration of Ancient Murals", IEEE
Transactions on Knowledge and Data Engineering, Vol. 15, No. 5, 2003.
[12] M. Luo, Y.F. Ma, H.J. Zhang, "A Spatial Constrained K-Means
Approach to Image Segmentation", Proc. the 2003 Joint Conference of
the Fourth International Conference on Information, Communications
and Signal Processing and the Fourth Pacific Rim Conference on
Multimedia, Vol. 2, pp. 738-742, 2003.
[13] Y.M. Cheung, "k*-Means: A new generalized k-means clustering
algorithm", Pattern Recognition Letters, Vol. 24, pp. 2883-2893, 2003.
[14] A.R. Barakbah, K. Arai, "Identifying moving variance to make
automatic clustering for normal dataset", Proc. IECI Japan Workshop
(IJW), Tokyo, 2004.
[15] H.M. Lotfy, A.S. Elmaghraby, "CoIRS: Cluster-oriented Image
Retrieval System", Proc. 16th IEEE International Conference on Tools
with Artificial Intelligence (ICTAI), pp. 224-231, 2004.
[16] N.J. Kwak, D.J. Kwon, Y.G. Kim, J.H. Ahn, "Color image segmentation
using edge and adaptive threshold value based on the image
characteristics", Proc. International Symposium on Intelligent Signal
Processing and Communication Systems (ISPACS), pp. 255-258. 2004.
[17] S.S. Khan, A. Ahmad, "Cluster center initialization algorithm for Kmeans
clustering", Pattern Recognition Letters, Vol. 25, pp. 1293-1302,
2004.
[18] A.R. Barakbah, A. Helen, "Optimized K-means: an algorithm of initial
centroids optimization for K-means", Proc. Seminar on Soft Computing,
Intelligent System, and Information Technology (SIIT), Surabaya, 2005.
[19] K.L. Priddy, P.E. Keller, Artificial Neural Networks, pp. 16-17, SPIE
Publications, 2005.
[20] A.M. Us├│, F. Pla, P.G. Sevila, "Unsupervised Image Segmentation
Using a Hierarchical Clustering Selection Process", Structural,
Syntactic, and Statistical Pattern Recognition, Vol. 4109, pp. 799-807,
2006.
[21] A.Z. Arifin, A. Asano, "Image segmentation by histogram thresholding
using hierarchical cluster analysis", Pattern Recognition Letters, Vol. 27,
no. 13, pp. 1515-1521, 2006.
[22] B. Mičušík, A. Hanbury, "Automatic Image Segmentation by
Positioning a Seed*", ECCV 2006, Part II, LNCS 3952, Springer
Berlin/Heidelberg, pp. 468-480, 2006.
[23] J. Chen, J. Benesty, Y.A. Huang, S. Doclo, "New Insights Into the Noise
Reduction Wiener Filter", IEEE Transactions on Audio, Speech, and
Language Processing, Vol. 14, No. 4, 2006.
[24] Y. Pan, J.D. Birdwell, S.M. Djouadi, "Bottom-Up Hierarchical Image
Segmentation Using Region Competition and the Mumford-Shah
Functional", Proc. 18th International Conference on Pattern Recognition
(ICPR), Vol. 2, pp. 117-121, 2006.
[25] L. Jin, D. Li, "A Switching vector median based on the CIELAB color
space for color image restoration", Signal Processing, Vol. 87, pp.1345-
1354, 2007.
[26] A.R. Barakbah, Y. Kiyoki, "A Pillar Algorithm for K-Means
Optimization by Distance Maximization for Initial Centroid
Designation", IEEE Symposium on Computational Intelligence and Data
Mining (CIDM), Nashville-Tennessee, 2009.
[27] A.R. Barakbah, Y. Kiyoki, "An Image Database Retrieval System with
3D Color Vector Quantization and Cluster-based Shape and Structure
Features", The 19th European-Japanese Conference on Information
Modelling and Knowledge Bases, Maribor, 2009.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49496", author = "Ali Ridho Barakbah and Yasushi Kiyoki", title = "A New Approach for Image Segmentation using Pillar-Kmeans Algorithm", abstract = "This paper presents a new approach for image
segmentation by applying Pillar-Kmeans algorithm. This
segmentation process includes a new mechanism for clustering the
elements of high-resolution images in order to improve precision and
reduce computation time. The system applies K-means clustering to
the image segmentation after optimized by Pillar Algorithm. The
Pillar algorithm considers the pillars- placement which should be
located as far as possible from each other to withstand against the
pressure distribution of a roof, as identical to the number of centroids
amongst the data distribution. This algorithm is able to optimize the
K-means clustering for image segmentation in aspects of precision
and computation time. It designates the initial centroids- positions
by calculating the accumulated distance metric between each data
point and all previous centroids, and then selects data points which
have the maximum distance as new initial centroids. This algorithm
distributes all initial centroids according to the maximum
accumulated distance metric. This paper evaluates the proposed
approach for image segmentation by comparing with K-means and
Gaussian Mixture Model algorithm and involving RGB, HSV, HSL
and CIELAB color spaces. The experimental results clarify the
effectiveness of our approach to improve the segmentation quality in
aspects of precision and computational time.", keywords = "Image segmentation, K-means clustering, Pillaralgorithm, color spaces.", volume = "3", number = "11", pages = "1901-6", }
