Research on the novel pattern clustering algorithm based on particle swarm optimized adaptive wavelet neural network model

Image segmentation is an integral part of critical image processing applications. Segmentation involves removal of a region of interest from the background. Recent researches in segmentation incorporate clustering algorithms for separation or removal of regions of interest. Prominent segmentation algorithms include K — means which segment the region from the background and further median filtering could be utilized to remove the unwanted regions in the segmented image. This research paper utilizes an adaptive wavelet neural network model with training or learning process optimized by the particle swarm optimization algorithm. The proposed algorithm has been tested and experimental results indicate a high precision of segmentation when compared with the conventional techniques.

[1]  Zarita Zainuddin,et al.  A Hybrid Algorithm for the Initialization of Wavelet Neural Networks: Application in Epileptic Seizure Classification , 2013 .

[2]  Mohamed Ben Ahmed,et al.  Simultaneous Clustering: A Survey , 2011, PReMI.

[3]  Jiang Sheng-yi.  An Enhanced k-means Clustering Algorithm , 2006 .

[4]  Farhat Roohi.  Artificial Neural Network Approach to Clustering , 2013 .

[5]  Sandro Vega-Pons,et al.  A Survey of Clustering Ensemble Algorithms , 2011, Int. J. Pattern Recognit. Artif. Intell..

[6]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[7]  Rahib Hidayat Abiyev,et al.  Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction , 2011, Neural Computing and Applications.

[8]  VASSILIS S. KODOGIANNIS,et al.  A Clustering-Based Fuzzy Wavelet Neural Network Model for Short-Term Load Forecasting , 2013, Int. J. Neural Syst..

[9]  Nouman Azam,et al.  Comparison of term frequency and document frequency based feature selection metrics in text categorization , 2012, Expert Syst. Appl..

[10]  V. Kavitha,et al.  Clustering Time Series Data Stream - A Literature Survey , 2010, ArXiv.

[11]  Mohammed Awad,et al.  Approximating i/o data using wavelet neural networks: control the position of mother wavelet , 2012, Int. Arab J. Inf. Technol..

[12]  Hui Xiong,et al.  K-Means-Based Consensus Clustering: A Unified View , 2015, IEEE Transactions on Knowledge and Data Engineering.

[13]  Ramzi A. Haraty,et al.  An Enhanced k-Means Clustering Algorithm for Pattern Discovery in Healthcare Data , 2015, Int. J. Distributed Sens. Networks.

[14]  Patricio A. Vela,et al.  A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm , 2012, Expert Syst. Appl..

[15]  Zhang Chun-ping,et al.  Research on K-means Clustering Algorithm , 2011 .

[16]  S. Jyothi,et al.  A Survey on Pattern Recognition using Fuzzy Clustering Approaches , 2013 .