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Complex Networks for Representation and Characterization of Object

Complex Networks for Representation and Characterization of Object. For CS790g Project Bingdong Li 11/9/2009. Outline Of Methodology. Re-introduce Traditional Approaches Proposed Methods Issues Summary Questions and Comments. Re-introduce Traditional Approaches .

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Complex Networks for Representation and Characterization of Object

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  1. Complex Networks for Representation and Characterization of Object For CS790g Project Bingdong Li 11/9/2009

  2. Outline Of Methodology Re-introduce Traditional Approaches Proposed Methods Issues Summary Questions and Comments

  3. Re-introduce Traditional Approaches • External characteristics: • boundary and shape • chain codes, polygonal approximations, skeletons • Internal characteristics: • color and texture, • statistical approaches, structural approaches, spectral approaches • Both external and internal characteristics. Source: CS674 Image Processing Lecture

  4. Proposed Methods:Representation • Define the network • a regular lattice network in 2-D background, each node is connected to its nearest neighbors depending on the Euclidean distance • Each node is addressed by its normalized degree Source: CS674 Image Processing Lecture

  5. Proposed Methods:Representation • Two pixel A and B on the contour A <-->B <==> d(A, B) <= r and d(A, B) >= n • r is the shape control threshold • n is noise control threshold • d is the normalized distance • k is the average degree

  6. From Raw Image

  7. To Matrices

  8. Proposed Methods:Representation

  9. Proposed Methods:Characterization • Network similarity algorithm: structural similarities Mehler, Alexander(2008) 'STRUCTURAL SIMILARITIES OF COMPLEX NETWORKS: A COMPUTATIONAL MODEL BY EXAMPLE OF WIKI GRAPHS',Applied Artificial Intelligence,22:7,619 — 683

  10. Algorithm • Raw image • Segmentation • Build the complex network • Classifying the object using network similarity algorithm

  11. Expected Results • In most case, it will be a small world network

  12. Expected Results • A methods for object classification that • Leverage complex network technology • Represent the geometric information • Represent the spatial information • Invariant to rotation • Invariant to translation

  13. Issues • the photometric information was not represented • Thresholds

  14. Summary • In this project, we tried a new approach for representation and characterization of object • Firstly, traditional approaches and complex network related approaches are reviewed • Then, proposed a new methods, its definition of network, characterization, and algorithm to classify an object

  15. Questions and Comments

  16. Thanks

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