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Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images
 

Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images

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The presentation of a paper entitled "Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images" to be presented in ICDAR 2013, Washingthon, DC, USA (August ...

The presentation of a paper entitled "Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images" to be presented in ICDAR 2013, Washingthon, DC, USA (August 25h-28th, 2013, on August 27th, 2013.

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    Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images Presentation Transcript

    • Reza FARRAHI MOGHADDAM, Fereydoun FARRAHI MOGHADDAM and Mohamed CHERIET Synchromedia Laboratory, ETS, Montreal (QC), Canada H3C 1K3 imriss@ieee.org, rfarrahi@synchromedia.ca, ffarrahi@synchromedia.ca, mohamed.cheriet@etsmtl.ca ICDAR 2013, Washington, DC, USA, August 25th-28th, 2013
    • Outline  Why Ensemble of Experts (EoE) framework?  EoE vs. Ensemble of Classifiers (EoC)  The big picture  Notations  Endorsements and the Endorsement Graph  The selection process  Calculation of the EoE result and its variations  Use cases  Conclusions and future prospects  Any questions!
    • Why Ensemble of Experts (EoE) framework?  In recent years, a large number of binarization methods have been developed, but almost all suffer from varying performance, generalization and strength against different benchmarks.  There is, and will be, no winner approach in short (or even in long) term because of complexity of study subjects (document and manuscript images) and also because of new processing goals.  In this work, to leverage on all these methods of varying performance and interrelations, the ensemble of experts (EoE) framework is introduced, to efficiently combine their outputs toward an output of higher performance.  The EoE framework can also be applied to other decision making problems:  Medical image segmentation  Parliament setting  Opinion fraud detection  However, caution should be taken when working with smart experts, such as humans, because they could collectively adjust their behavior, having prior access to the rules of an EoE-based framework, to win the ensemble’s result.
    • Ensemble of Experts vs. Ensemble of Classifiers EoE EoC  En ensemble.  It work on a “set” of problems not just one problem  Every member is “free” to devise its own approach to modeling and concluding its opinion on each problem.  It could be seen as an enabler toward featureless approaches.  Performance evaluation is not easy and straightforward.  En ensemble.  It (usually) works on one problem at a time.  Every member works on the “regularized” representations of problem, i.e., the feature vectors.  Performance comparison is more accurate and trustable because of regularization approach used.
    • Basics of the EoE framework  The proposed EoE framework offers a new expert selection process from an ensemble, by introducing three concepts: confidentness, endorsement and schools of experts.  The EoE framework tries to combine the outputs of an ensemble of related and unrelated experts using consolidation and selection concepts toward an less-biased opinion.  Endorsement graph:  is defined based on the relations among the confidentness of the experts on their own opinions across the ensemble.  Two generic selection principles:  Consolidation of saturated opinions  Selection of schools of experts  For binarization methods, which lack the confidentness values, a confidentness map is defined.  After building the endorsement graph of the ensemble for an input document image based on the confidentness of the experts, the saturated opinions are consolidated, and then the schools of experts are identified by thresholding the consolidated endorsement graph.  The framework was successfully applied on the H-DIBCO’12 dataset. However, it is not limited only to handwritten documents.  A variation of the framework, in which no selection is made, is also introduced that combines the outputs of all experts using endorsement-dependent weights (called EwEoE).  Many aspects of the proposed framework could be improved.
    • EoE Framework: The Big Picture EoE Framework is based on three concepts of Confidentness, Endorsement, and School of Expert 0. Assemble the Ensemble of Experts 1. Acquire the Set of Problems 2. Get the Opinions of experts on problems 3. Calculate the Confidentness of each expert on each problem 4. Calculate the Endorsement Graph among experts 5.1 Consolidate highly-similar experts (Reduce Bias) 5.2 Calculate the Schools of Experts (clusters of experts) 6. Calculate the EoE result by considering only members of the schools 7. Go back to step 1 to process a new set of problems
    • Notations and application of the EoE Framework to document binarization Currently, the methods do not provide any estimation of their confidentness on individual pixels EoE framework notation Equivalent in document binarization 1 An expert A binarization method 2 An Ensemble of Experts A set of binarization methods (can be the same method with different parameters) 3 A problem Binarization of a pixel 4 A set of problems Binarization of an image as a set of pixels 5 Opinion of an expert on a problem Binarization value of a method on a pixel 6 Confidentness of an expert on its opinion <<To Be Defined>> 7 Endorsement (of expert A by expert B) Endorsement (of method A by method B) 8 Endorsement graph Endorsement graph
    • Endorsement Graph Weights The relation among confidentness maps on all pixels is used to define the weight of corresponding edge on the endorsement graph Confidentness of a on pixel i masked by that of b Endorsement b  a
    • EoE and EwEoE means The selection processEoE-adjusted mean output EwEoE-adjusted mean output “Regular” mean output
    • An example of a highly-biased ensemble 84 experts using the Gb Sauvola method[1] 1. The Endorsement Matrix 2. Consolidated Endorsement Matrix 3. The selected experts 1. The Endorsement Graph 2. Consolidated Endorsement Graph 3. The selected experts (Graph) [1] Farrahi Moghaddam, Reza, and Mohamed Cheriet. "A multi-scale framework for adaptive binarization of degraded document images." Pattern Recognition 43, no. 6 (2010): 2186-2198. DOI: 10.1016/j.patcog.2009.12.024
    • EoE Framework Performance (1): H-DIBCO’12 Ensemble on H-DIBCO’12 datasetOriginal Endorsement Graph of H-DIBCO’12 for H12 The performance Final Schools of Expert for H12
    • EoE Framework Performance (2): Gb Sauvola ensemble (84 experts) on H-DIBCO’12 datasetOriginal Endorsement Graph of H-DIBCO’12 for H12 Final Schools of Expert for H12 EoE output for H05 “Regular” output for H05The performance
    • EoE Framework Performance (3): Laplacian- energy[2] ensemble on H-DIBCO’12 dataset The performance H-DIBCO’12:H05 H-DIBCO’12:H09 H-DIBCO’12:H14 [2] Howe, Nicholas R. "Document binarization with automatic parameter tuning." International Journal on Document Analysis and Recognition (IJDAR) (2012): 1-12. DOI: 10.1007/s10032-012-0192-x
    • Conclusions: The EoE framework Summary Future Prospects  The ensemble of experts (EoE) framework is introduced, to efficiently combine the opinion of experts methods on a set of problems.  It is based on  Confidentness  Endorsement  Schools of experts  The EoE framework:  combines the outputs of an ensemble of related and unrelated experts using consolidation and selection concepts toward reducing the bias of opinions.  Endorsement graph is defined based on the confidentness of the experts.  Two generic principles of the EoE framework:  Consolidation of saturated opinions  Selection of schools of experts  It has been applied to the H-DIBCO’12 database using various ensembles of experts: H-DIBCO’12 participants, Gb Sauvola, and Laplacian-energy.  Generalization to other applications in other decision making problems:  Medical image segmentation  Parliament setting  Opinion fraud detection  Improving the selection processes:  Especially the consolidation step  Adding another level of selection by selecting one school out of all the EoE schools  Improving the endorsement definition  Standardization of the confidentness value as the secondary output of an expert (a binarization method) in addition to its opinion value (binary output).
    • Thank you; any questions! imriss@ieee.org, rfarrahi@synchromedia.ca Synchromedia Lab ETS NSERC http://arxiv.org/abs/1305.2949