Immersive visualization of visual data using nonnegative matrix factorization

Over the last two decades, dimension reduction for visualization has gained a high amount of attention in visual data mining where the data is represented by high-dimensional features. Basically, this approach leads to an unbalanced and occluded distribution of visual data in display space, giving rise to difficulties in browsing the data. In this paper we propose an approach for the visualization of image collections in such a way as (1) images are not occluded by each other, and the provided space is used as much as possible; (2) the similar images are positioned close together; (3) an overview of data is feasible. To fulfill these requirements, we propose to use regularized Nonnegative Matrix Factorization (NMF) controlled by parameters to reduce the dimensionality of data. Experiments performed on optical and radar images confirm the flexibility of proposed method in visualizing large-scale visual data. Finally, an immersive 3D virtual environment is suggested, to visualize the images, to allow the user to navigate and explore the data.

[1]  Qi Tian,et al.  Visualization and User-Modeling for Browsing Personal Photo Libraries , 2004, International Journal of Computer Vision.

[2]  Xiaojun Wu,et al.  Graph Regularized Nonnegative Matrix Factorization for Data Representation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[4]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[5]  Yanwei Pang,et al.  Learning Regularized LDA by Clustering , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Tanji Hu,et al.  Summarizing tourist destinations by mining user-generated travelogues and photos , 2011, Comput. Vis. Image Underst..

[7]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[8]  David L. Sheinberg,et al.  Visual object recognition. , 1996, Annual review of neuroscience.

[9]  Desney S. Tan,et al.  EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers , 2009, CHI.

[10]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[11]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[12]  Mihai Datcu,et al.  Immersive Interactive Information Mining with Application to Earth Observation Data Retrieval , 2013, CD-ARES.

[13]  Mihai Datcu,et al.  Assessment of dimensionality reduction based on communication channel model; application to immersive information visualization , 2013, 2013 IEEE International Conference on Big Data.

[14]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Stephen J. McKenna,et al.  Visualizing Image Collections Using High-Entropy Layout Distributions , 2010, IEEE Transactions on Multimedia.

[16]  Geoffrey E. Hinton,et al.  Stochastic Neighbor Embedding , 2002, NIPS.

[17]  Marcel Worring,et al.  Interactive access to large image collections using similarity-based visualization , 2008, J. Vis. Lang. Comput..

[18]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[19]  Mihai Datcu,et al.  Farness preserving Non-negative matrix factorization , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[20]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[21]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[23]  Xuelong Li,et al.  Ranking Graph Embedding for Learning to Rerank , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Jiang-She Zhang,et al.  Learning latent features by nonnegative matrix factorization combining similarity judgments , 2015, Neurocomputing.

[25]  A. Rényi.  On Measures of Entropy and Information , 1961 .

[26]  Nicolas Gillis,et al.  Two algorithms for orthogonal nonnegative matrix factorization with application to clustering , 2012, Neurocomputing.

[27]  Desney S. Tan,et al.  CueFlik: interactive concept learning in image search , 2008, CHI.

[28]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[29]  Trevor F. Cox,et al.  Metric multidimensional scaling , 2000 .

[30]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[31]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .