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Multicue Fusion Image Segmentation: Integrating Conditional Random Fields and Gabor Filters

This paper delves into advanced image segmentation techniques utilizing multicue fusion. It introduces key principles of image segmentation and outlines the main processes involved, including Conditional Random Fields (CRFs) for optimizing segmentation through energy minimization. The method employs Gabor filters for texture likelihood estimation and Mean Shift for color likelihood estimation. By constructing energy functions and applying graph cuts, effective segmentation is achieved. The study emphasizes the integration of color and texture information in achieving high-quality segmentation results.

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Multicue Fusion Image Segmentation: Integrating Conditional Random Fields and Gabor Filters

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  1. Image Segmentation based on multicue fusion XiaoweiGeng 2007.09.24

  2. Content • Introduction about image segmentation • image segmentation priciples • Main Process • Related Results

  3. Main Principles • Conditional Random Fields Model • Mean Shift For color likelihood estimation • Gabor Filters For texture likelihood estimation • Energy Optimized by Graph Cuts

  4. Conditional Random Fields • Definition (1) G=(V,E) is a graph (2) Y is indexed by the vertices of G . (3) the random variables obey the Markov property with respect to the graph, when conditioned on X : (X,Y) is a conditional random field. where means that w and v are neighbors in G.

  5. Conditional Random Fields • Characteristics where x is data information,y is label information,and y|s is the set of components of y associated with the vertices in subgraph S.

  6. Kernel Density Estimation (1) Kernel density estimator (2) two methods of H chosen in practice

  7. Mean Shift • A method to find modes of function

  8. Gabor Filters • Biological enlightenment • The Gabor Filters characteristics

  9. Gabor Filters

  10. Image Segmentation based on CRF • Construct Energy Function

  11. Energy Function • Our Energy Function As the similar presentation of MRF,we still use the classification to explain every term.

  12. Color Likelihood Term • Similar to Bayesian explanation,we define Here ,xi is the mean value of background or foreground sample

  13. Texture Likelihood Term • First , get the feature image by Gabor filters • second, use mean shift to filter the interesting regions to get the mean feature vector • Third ,use kernel density estimation to get the texture likelihood term

  14. Gabor Filter banks

  15. Gabor Filters

  16. Smooth Term • Smooth Term in order to make the results more smooth,we define the color smooth and texture smooth Terms as:

  17. Energy Optimize • Graph Cuts

  18. Some Results

  19. Some Results

  20. Some Results

  21. Some Results

  22. Some Results

  23. Some Results

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