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Interactive Pattern Discovery with Large Imaging Databases Tin Kam Ho Computing Sciences Research Center Bell Labs, Lucent Technologies In collaboration with David Wittman, J. Anthony Tyson of UC Davis Samuel Carliles, William O’Mullane, Alex Szalay of JHU What Is the Story in this Image? Solving the Puzzle with a 3-step Approach 1. Describe each symbol shape with a numerical vector [23 12 17 28 11 …] 2. Find clusters of symbol shapes 3. Interpret each cluster using context 10.10.10 51.37.50.54.41.35.37 39.47.33.44 13.13 33.52.6.52 83.65.73.68 73.84 72.65.83 83.69.84 65 71.79.65.76 79.70 82.69.83.84.79.82.73.78.71 83.69.82.86.73.67.69 79.78 73.84.83 76.79.78.71.13.68.73.83.84.65.78.67.69 78.69.84.87.79.82.75 70.65.83.84.69.82 84.72.65.78 84.72.69 83.89.83.84.69.77 73.84.83.69.76.70 68.73.83.67.79.78.78.69.67.84.83 67.65.76.76.83 65.70.84.69.82 65 67.65.66.76.69 66.82.69.65.75.14 *** SERVICE GOAL -- AT&T said it has set a goal of restoring service on its long-distance network faster than the system itself disconnects calls after a cable break. Tracking Intensive Rain Cells in Radar Images The Deep Lens Survey (Tyson, Wittman, … ) BVRz to 26 mag over 28 sq. degree http://dls.physics.ucdavis.edu/ Weak Gravitational Lensing Uses distortion of background galaxies to map foreground mass concentrations J.A. Tyson, DLS 2002 Catalog of Extracted Objects Stars or Galaxies? J.A. Tyson, DLS 2002 • Discrimination task depends on tiny differences in color and shape • Survey is to an unpreceded depth: most objects have never been observed before and nobody knows their true classification • How does one build confidence on the results of the classifier? • Need to correlate several perspectives: object characteristics in the color space, shape parameters, the brightness statistics • Visualization can help verify correctness of preprocessing steps, clean up undesirable artifacts, choose relevant samples, spot explicit patterns, select useful features, and suggest algorithms and models The Virtual Observatory http://www.us-vo.org/ http://www.ivoa.net/ Essential Steps in Automatic Pattern Recognition Samples Supervised learning Unsupervised learning features Clustering Cluster Validation features Feature Classifier Extraction Training classifier feature 2 Classification Cluster Interpretation class membership feature 1 Data Relationships Across Multiple Feature Sets Data Mining Feature Set A Simulation Analysis Set B Unknown Relationship Clustering Parameters Responses Feature Computation Filtering, Clustering Key Algorithms • Clustering: find natural groups in data, construct index structures to facilitate proximity queries • Dimensionality reduction: embed high-dimensional data in 2D displays • Navigation: traverse index structures in systematic ways Clustering Methods • Model based Clustering identification of finite mixtures • Partitional Clustering divides data set into N mutually exclusive subsets • Hierarchical Clustering top-down procedures: tree splitting bottom-up, agglomerative procedures: merge similar clusters successively Similarity / Clustering of Objects from Different Perspectives • Objects can be described by many types of attributes: position, weight, shape, spectrum, time variability, … • Meaningful similarity metric exists only for the same type of attributes • Clusters found from one perspective need to be correlated to those from others e.g. Are the objects similar in color also similar in shape? Color clusters Shape clusters Exploratory Tools Needed To bring in domain expertise, interpretation context To visualize data or classifier geometry To track point/class correlations To test tentative classifications To compare groupings from different perspectives To relate numerical data to other data types To facilitate systematic, repeatable explorations Mirage for Interactive Pattern Recognition http://www.cs.bell-labs.com/who/tkh/mirage Data Display in Linked Views • Show patterns in histograms, scatter plots, parallel coordinates, tables, and images Selection and Tracking • Select points in any view, broadcast to all others Traversal of Data Structures • Walk in histograms, cluster graphs or trees, echoed in all other views Graphical Utilities Intuitive Graphical Tool for • Command Scripts Exploratory Data Analysis Visualization of Clusters and Classes Correlation of Proximity Structures Manual or Automatic Classification • Open multiple-page plots with arbitrary configuration Run prepared groups of operations as an animation Software Features • Based on Java Swing library • Intuitive, easy-to-use graphical operations • Mutiple-page, arbitrary plot configurations • Online or offline cluster analysis • GUI or Script driven command execution • Database interface via JDBC • Ready to be adapted for on-line monitoring • Ready to be integrated with database access and decision support systems Design Motivated by the Needs Interactive plays, intuitive operations to bring domain experts into the loop Multiple types of plots, extensible for more to visualize data or classifier geometry Linked views, traversal actions to track point/class correlations Highlights, colors to test tentative classifications Projection to arbitrary subspaces to compare groupings in different perspectives Linking data with images to relate numerical data to other types Command scripting to facilitate systematic, repeatable explorations Challenges for the Analysis Tool • Separate treatment of non-comparable groups of variables • Versatile visualization utilities allowing many perspectives • Support for exploratory discovery across diverse data types • Integrate manual & automatic pattern recognition methods Also, a good tool should -- leverage existing visualization and analysis methods -- enable continued growth: new visualization, analysis tools -- support interface with existing databases -- be scalable in data volume and processing speed Towards Extensibility VO Data Archives Data Access Clients Cone Search, CAS External Rendering Code Custom Data Views FITS viewer, … Mirage Core Extinction Calculator Data Analysis Methods Web Services Python? Matlab? Data Exchange Pipes Other Analysis Platforms VO Enabled Mirage (with Samuel Carliles, William O’Mullane, and Alex Szalay) VO Enabled Mirage • http://skyservice.pha.jhu.edu/develop/vo/mirage/ • Load VOTable data and perform VO Cone/SIAP and SDSS CAS searches using IVOA Client Package • Astronomical imaging module loads FITS images using JSky classes, supporting image operations: Select data points and broadcast selection to other views. Cut levels. Colormap. SAO DS9-style brightness/contrast enhance. Zoom. Extinction Web Service (with Chris Miller, Simon Krughoff) Using DIRBE/IRAS Dust Maps by Schlegel et al. Mirage Core Object selection Extracts RA,DEC,[mag] from Mirage data set Positions, mags Positions, mags, filterIDs SOAP client calls Extinction server Enhanced data set Result stream Extinction Service E(b-v), dered_mags Merges results with Mirage data set More at NVO Public Release 1.0 205th Meeting of the American Astronomical Society 9-13 January 2005 San Diego, CA Wednesday, 12 January Astronomical Research with the Virtual Observatory Analysis of Simulations of Control Dynamics in Optical Transport Systems (with the FROG collaboration) Fiber link Head End Terminal Repeater Repeater Gain Equalizer Repeater Signal Spectrum with noise floor Repeater Tail End Terminal Monitoring Network Traffic (With Marina Thottan, Ken Swanson) Software tool for online monitoring and analysis of QoS in IP networks • continuously monitors traffic statistics at edge and core devices • synthesizes statistics in real time to obtain network-wide QoS status and general network element health indicators • Mirage refreshes displays on alerts of database updates via Java Messaging Service Provisioning SLA verification Billing SEQUIN SNMP polling MPLS IP Core (QoS-guaranteed paths) DiffServ Edge (aggregation and classification)