Fourth Year Project - Presentation

1. Fourth Year Project - Presentation • Project Title: • Content Based Image Retrieval (CBIR) • Presenters: • Rami Al Tayeche • Ahmed Khalil • Supervisor: • Professor Aysegul Cuhadar

2. Presentation - Outline • Introduction • What is CBIR? • Applications of CBIR • Our Approach • Colour • Texture • Shape • Where We Are • Conclusion • Questions and Answers

3. Introduction - What is CBIR? • The term [CBIR] describes the process of retrieving desired images from a large collection on the basis of features (such as colour, texture and shape) that can be automatically extracted from the images themselves.

4. Introduction - Reasons for its development • In many current applications with large image databases, traditional methods of image indexing have proven to be insufficient. For example; Finger print scanning cannot be done using a keyword search.

5. Introduction - Applications • Automatic face recognition systems

6. Introduction - Applications • Medical Image Databases

7. Introduction - Applications • Trademark Image Registration

8. Our Approach - Image Features • The image features that we will be focusing on, for image retrieval are: • Colour • Texture • Shape Other primitive features not considered are: • Spatial location • Pixel intensity

9. Our Approach - Colour

10. Our Approach - Colour Histograms

11. Our Approach - Colour Maps

12. Our Approach - Minkowski Distance

13. Our Approach - Quadratic Distance

14. Our Approach - Similarity Matrix

15. Our Approach - Implementation Matlab Code

16. Our Approach - Texture • Texture is that innate property of all surfaces that describes visual patters, and that contain important information about the structural arrangement of the surface and its relationship to the surrounding environment. What is Texture?

17. Our Approach - Texture • Examples: Finger print Texture Brick Texture Clouds Texture Rocks Texture

18. Our Approach - Texture Properties • Co-occurrence matrix: • Based on the orientation and distance between image pixels. • From it we obtain statistics that represent: • Coarseness • Contrast • Directionality • Linelikeness • Regularity • Roughness Texture properties

19. Our Approach - Wavelet Texture • Wavelet Texture: • Textures can be modeled as quasi-periodic patterns with spatial/frequency representation. The wavelet transform transforms the image into a multi-scale representation with both spatial and frequency characteristics.

20. Our Approach - Tree Algorithm • Algorithm: Tree-Structured Wavelet Transform • Decompose the image into four sub-images • Calculate the energy of all decomposed images at the same scale, using: • If the energy of a sub-image is significantly larger, repeat from step 1.

21. Our Approach - Tree Algorithm

22. Our Approach - 1st Decomposition

23. Our Approach - 2nd Decomposition

24. Our Approach - Classification • Algorithm: Euclidean Distance Classification • Decompose query image. • Get the energies of the first dominant k channels. • For image i in the database obtain the k energies. • Calculate the Euclidean distance between the two sets of energies, using: • Increment i. Repeat from step 3.

25. Our Approach - Shape • Shape is the characteristic surface configuration that outlines an object giving it a definite distinctive form. • Fairly well-defined concept. What is Shape?

26. Our Approach - Shape • Examples:

27. Our Approach - Shape Features • Aspect ratio • Circularity • Moment invariants • Sets of consecutive boundary segments

28. Our Approach - Shape Extraction • Techniques under consideration: • Fourier Descriptor • Moment Invariants • Directional Histograms

29. Where We Are

30. Image Database

31. Conclusion • What is CBIR? • The retrieval of images from a database based on content features such as colour, texture and shape. • Reasons for its developments • Insufficiency in certain applications

32. Conclusion • Applications • Finger print scanning systems • Automatic face recognition systems • Medical image databases • Trademark image registration

33. Conclusion • Our Approach • Colour • Texture • Shape • Where we are • In the phase of understanding and implementing shape.