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

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Presentation transcript:

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 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

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

Proposed Methods: Representation Two pixel A and B on the contour A B 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

From Raw Image

To Matrices

Proposed Methods: Representation

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

Algorithm 1.Raw image 2.Segmentation 3.Build the complex network 4.Classifying the object using network similarity algorithm

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

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

Issues the photometric information was not represented Thresholds

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

Questions and Comments

Thanks