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Barcelona Forum on Ph.D. Research in Communications, Electronics and Signal Processing

Barcelona Forum on Ph.D. Research in Communications, Electronics and Signal Processing 21 st October 2010. OP2: Information Theoretical Region Merging Approaches and Fusion of Hierarchical Image Segmentation Results Felipe Calderero, Thesis Advisor: Ferran Marqués. Outline. Introduction

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Barcelona Forum on Ph.D. Research in Communications, Electronics and Signal Processing

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  1. Barcelona Forum on Ph.D. Research in Communications, Electronics and Signal Processing 21st October 2010 OP2: Information Theoretical Region Merging Approaches and Fusion of Hierarchical Image Segmentation ResultsFelipe Calderero, Thesis Advisor: Ferran Marqués

  2. Outline • Introduction • Information Theoretical Region Merging • Cooperative Region Merging • Conclusions

  3. 1. What is image segmentation? • Image Segmentation • Partition of the image into regions (disjoint sets of spatially contiguous pixels) • Key step in image analysis • Semantically, first level of abstraction • Practically, reduction of primitives • But image segmentation is a difficult task…

  4. 1. An ill-posed problem… • Image Segmentation is an ill-posed problem • A unique solution may not exist • Different levels of detail • Same level of detail

  5. 1. An ill-posed problem… • Image Segmentation is an ill-posed problem • A unique solution may not exist • Different levels of detail • Same level of detail Hierarchical Segmentation Approaches Fusion of (Hierarchical) Segmentation Results

  6. 1. PhD Thesis Objectives • Objective 1: Hierarchical Segmentation Approaches • Objective 2: Fusion of (Hierarchical) Segmentation Results Provide an unsupervised hierarchical solution to the segmentation of generic images Design a generic and scalable segmentation scheme to fuse in an unsupervised manner hierarchical segmentation results

  7. 1. PhD Thesis Approach • Solutions to Objective 1 and Objective 2 • Generic • No a priori information • Hierarchical solution • Unsupervised Bottom-up hierarchy [Marr82] Region Merging Techniques Hierarchy of most representative partitions at different levels of detail

  8. 1. PhD Thesis Contributions • Contribution 1: Hierarchical Segmentation Approaches • Contribution 2: Fusion of (Hierarchical) Segmentation Results • Information Theoretical Region Merging Techniques (IT-RM) • Cooperative Region Merging Scheme (CRM)

  9. 2. Region Merging Techniques Binary Partition Tree (BPT) [Garrido99] Efficiency of computation and representation • Region Merging • Hierarchical bottom-up segmentation approaches • Specified by • Region Model • Merging Criterion • Merging Order • Partition Selection Criterion G G Selection Criterion F F E Hierarchy creation A E E B Unsupervised mode A Relevant partition extraction A B C D D C B

  10. 2. Information Theory Region Merging • Information Theoretical Region Merging (IT-RM) • Statistical and information theoretical framework • Region Model • i.i.d / Markov region model • Merging Criteria • Kullback-Leibler / Bhattacharyya Criteria • Merging Order • Classical / Scale-based • Unsupervised mode: • Multiple partition selection criterion (statistically relevant)

  11. 2. IT-RM Applications • Semantic image analysis 1st Significant Partition 2nd Significant Partition Original

  12. 2. IT-RM Applications • Semantic image analysis (textures) 1st Significant Partition 2nd Significant Partition Original

  13. 2. IT-RM Applications • Object-based representation and analysis

  14. 3. Cooperative Region Merging • Motivation: Objective 2 • Most IT-RM techniques have similar and accurate performance… • Instead of selecting, why not combining the set of techniques? • Cooperative Region Merging (CRM) • Similar to a negotiation process in decision making

  15. 3. Cooperative Region Merging • Cooperative Region Merging • Segmentation results are computed independently by each technique (RM step) • A basic consensus or agreement is established between the set of techniques (FUSION step) • Steps 1 and 2 are repeated while further consensus is possible • Characteristics: parallel, scalable, hierarchical, unsupervised, flexible…

  16. 3. CRM Applications • Accuracy and robustness improvement • Combining different segmentation techniques Human Partition 3rd Median Partition 2nd Median Partition 1st Median Partition Original

  17. 3. CRM Applications • Fusion of heterogeneous information channels • Combining color and depth for object-based segmentation Disparity map 3rd Sign. Partition 2nd Sign. Partition 1st Sign. Partition Color image

  18. 3. CRM Applications • Scalability and flexibility of the fusion scheme • Fusion of multispectral band and vegetation classification RGB composition NDVI Vegetation extraction using bands: B, G, R, IR Vegetation extraction using bands: B, G, R, IR + PAN

  19. 4. Conclusions • Unsupervised segmentation of generic images: a challenge • Hierarchical Region Merging approach as a possible solution • Info. Theoretical Region Merging Segmentation • State-of-the-art results in unsupervised manner • Relevant image explanations at different levels of detail • Application independent object-based semantic tool • Cooperative Region Merging Information Fusion • Accuracy and robustness improvement in unsupervised manner • Scalable, flexible and generic scheme • Fusion of homogeneous/heterogeneous information channels

  20. Information Theoretical Region Merging Approaches and Fusion of Hierarchical Image Segmentation ResultsFelipe Caldererofelipe.calderero@upf.eduImage Processing Group Pompeu Fabra University (UPF) Barcelona, SpainFerran Marquésferran.marques@upc.eduImage Processing Group Universitat Politècnica de Catalunya Barcelona, Spain

  21. 4. Applications • CRM: Fusion of color and depth information

  22. 4. Applications • IT-RM: Semantic image analysis 1st Significant Partition 2nd Significant Partition Original

  23. 5. Conclusions • Information Theoretical Region Merging • State-of-the-art segmentation results without any assumption about the nature of the region • Unsupervised extraction of most relevant image explanations at different levels of detail • Application independent accurate tool for object-based representation and semantic analysis of generic images

  24. 5. Conclusions • Cooperative region merging • Global improvement of the accuracy and the stability of the segmentation results by combining different segmentation approaches • Parameter removal solution • Fusion of heterogeneous information channels • Flexibility to incorporate specificities of the fusion problem • A priori information about the fused sources (e.g. channel priority) • Joint segmentation and classification stages

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