Usage of Dissimilarity Measures and Multidimensional Scaling for Large Scale Solar Data Analysis

This work describes the application of several dissimilarity measures combined with multidimensional scaling for large scale solar data analysis. Using the first solar domain-specific benchmark data set that contains multiple types of phenomena, we investigated combinations of different image parameters with different dissimilarity measures in order to determine which combinations will allow us to differentiate our solar data within each class and versus the rest of the classes. In this work we also address the issue of reducing dimensionality by applying multidimensional scaling to our dissimilarity matrices produced by the previously mentioned combinations. By applying multidimensional scaling we can investigate how many resulting components are needed in order to maintain a good representation of our data (in a artificial dimensional space) and how many can be discarded in order to economize our storage costs. We present a comparative analysis between different classifiers in order to determine the amount of dimensionality reduction that can be achieve with said combination of image parameters, similarity measure and multidimensional scaling.

[1]  Andrew M. Kuhn.  Multivariate Statistical Methods in Quality Management , 2005, Technometrics.

[2]  Patrick J. F. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 2003 .

[3]  Pascal Fua,et al.  Texture Boundary Detection for Real-Time Tracking , 2004, ECCV.

[4]  Wei-Ying Ma,et al.  Learning similarity measure for natural image retrieval with relevance feedback , 2002, IEEE Trans. Neural Networks.

[5]  Rafal A. Angryk,et al.  An Experimental Evaluation of Popular Image Parameters for Monochromatic Solar Image Categorization , 2010, FLAIRS.

[6]  R N Shepard,et al.  Multidimensional Scaling, Tree-Fitting, and Clustering , 1980, Science.

[7]  C. Spearman.  The proof and measurement of association between two things. , 2015, International journal of epidemiology.

[8]  Zhong-ke Shi,et al.  Traffic Image Classification Method Based on Fractal Dimension , 2006, 2006 5th IEEE International Conference on Cognitive Informatics.

[9]  Wei-Ying Ma,et al.  Learning similarity measure for natural image retrieval with relevance feedback , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Hermann Ney,et al.  Features for image retrieval: an experimental comparison , 2008, Information Retrieval.

[11]  B. S. Manjunath,et al.  Dimensionality reduction using multi-dimensional scaling for content-based retrieval , 1997, Proceedings of International Conference on Image Processing.

[12]  Pablo García Rodríguez,et al.  Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics , 2005, Comput. Vis. Image Underst..

[13]  C. Tomasi.  The Earth Mover's Distance, Multi-Dimensional Scaling, and Color-Based Image Retrieval , 1997 .

[14]  V. Devendran,et al.  SVM Based Hybrid Moment Features for Natural Scene Categorization , 2009, 2009 International Conference on Computational Science and Engineering.

[15]  C. Spearman.  The proof and measurement of association between two things. By C. Spearman, 1904. , 1987, The American journal of psychology.

[16]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[17]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[18]  C. A. Fox.  An Information Retrieval System for Images from the Trace Satellite , 2008 .

[19]  Antoine Naud,et al.  Neural and Statistical Methods for the Visualization of Multidimensional Data , 2001 .

[20]  Rafal A. Angryk,et al.  On the effectiveness of fuzzy clustering as a data discretization technique for large-scale classification of solar images , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[21]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[22]  Bidyut Baran Chaudhuri,et al.  Texture Segmentation Using Fractal Dimension , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Rudolf Hanka,et al.  Similarity measures for histological image retrieval , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[24]  Valentina V. Zharkova,et al.  Feature Recognition in Solar Images , 2005, Artificial Intelligence Review.

[25]  James Ze Wang,et al.  Content-based image retrieval: approaches and trends of the new age , 2005, MIR '05.

[26]  A. Kandaswamy,et al.  Breast Tissue Classification Using Statistical Feature Extraction Of Mammograms , 2006 .

[27]  Alex Pentland,et al.  Fractal-Based Description of Natural Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.