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Challenges in Mining Large Image Datasets

Challenges in Mining Large Image Datasets. Jelena Tešić, B.S. Manjunath University of California, Santa Barbara http://vision.ece.ucsb.edu. Introduction. Data and event representation Meaningful data summarization Modeling of high-level human concepts Learning events

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Challenges in Mining Large Image Datasets

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  1. Challenges in Mining Large Image Datasets Jelena Tešić, B.S. Manjunath University of California, Santa Barbara http://vision.ece.ucsb.edu

  2. Introduction • Data and event representation • Meaningful data summarization • Modeling of high-level human concepts • Learning events • Feature space and perceptual relations • Mining image datasets • Feature set size and dimension • Size and nature of image dataset • Aerial Images of SB county • 54 images - 5428x5428 pixels • 177,174 tiles - 128x128 pixels Vision Research Lab

  3. Visual Thesaurus • Perceptual Classification • T=1; SOM dim. red. of input training feature space • Assign labels to SOM output • LVQ finer tuning of class boundaries • It T< number_of_iterations { T=T+1; go back to step 2. } else END. Perceptual and feature space brought together: same class (16) and class 17 • Thesaurus Entries Generalized Lloyd Algorithm 330 codewords Vision Research Lab

  4. x p distance q direction y Cρ(u,v) v u TEXTURE C O L O R SEC Spatial Event Cubes • Image tile raster space • Thesaurus entries • Spatial binary relation ρ • SEC face values • Multimode SEC Vision Research Lab

  5. SEC 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1566 0 0 0 0 0 0 0 8 0 1 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 1 0 1874 0 0 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 121 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 496 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 6 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 397 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 114 0 0 0 2 0 0 0 0 0 0 0 3825 2 0 0 0 0 0 0 8 0 0 0 0 5 3 50 0 0 0 72 0 0 0 2 0 0 0 1 4215 0 0 0 2 0 0 0 0 1 0 0 5 8 653 0 0 0 434 Cluster Analysis Visual Data Mining Vision Research Lab

  6. Generalized Apriori Find all sets of tuples that have minimum support Use the frequent itemsets to generate the desired rules Low-level mining Occurrence of the ocean in the image dataset 2D 3D Spatial Data Mining Vision Research Lab

  7. Ocean analysis Higher level Mining 890 434 653 Vision Research Lab

  8. Conclusion • Visual mining framework • Spatial event representation • Image analysis at a conceptual level • Perceptual knowledge discovery • Demos: • http://vision.ece.ucsb.edu/texture/mpeg7/ • http://nayana.ece.ucsb.edu/registration/ • Amazon forest DV 40 hours – 5tbytes Mosaics from 2 h Vision Research Lab

  9. Adaptive NN Search for Relevance Feedback • Relevance Feedback • learn user’s subjective similarity measures • Scalable solution • Explore the correlation of consecutive NN search • VA-file indexing • Feature space • Query • Distance Measure • - K nearest neighbors at iteration t • - distance between Q and the K-th farthest object • upper bound • - K-th largest upper bound of all approximations Vision Research Lab

  10. Adaptive NN Search for Relevance Feedback • If is a qualified one in its lower bound must satisfy • When , it is guaranteed that more candidates can be excluded as compared with traditional search Vision Research Lab

  11. vs. Their difference is larger at a coarser resolution vs. At coarser resolution, the estimate is better Performance Evaluation - 685,900 images Vision Research Lab

  12. Performance Evaluation Adaptive NN search • Utilizing the correlation to confine the search space • The constraints can be computed efficiently • Significant savings on disk accesses Vision Research Lab

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