Content Based Color Image Retrieval vi Wavelet Transformations

1. Information Retrieval Class Presentation Content Based Color Image Retrieval vi Wavelet Transformations May 2, 2012 Author: Mrs. Y.M. Latha Presenter: Mahbubur Rahman Advisor: Prof. Susan Gauch

2. Table of Contents • Introduction • Target Environment • Proposed CBIR • Wavelet Transform • Feature Extraction • Similarity Criteria • Progressive Retrieval Strategy • Experiment Result • Conclusion

3. Introduction • Content Based Image Retrieval • Database is huge • Retrieved the desired image from the database

4. Introduction • Content Based Image Retrieval • Images have specific features-horizontal or vertical lines • Image features are compared to find similar images Query image Feature extract to compare Database Image

5. Target Environment • Color Image Retrieval • Based on Object Visual contents of image • Color, Texture and Shape • Multimedia image with audio, text and video are not covered

6. Proposed CBIR • Wavelet Based CBIR • Indexing -wavelet decomposition then F-norm • Searching-wavelet decomposition, F-norm then similarity matching Searching Process Indexing Process

7. Wavelet Transform • Wavelet Transformation • Decompose using rescaling and keeping details of image

8. Wavelet Transform • Haar Wavelet Transform • Find out N/2 wavelet values and N/2 coefficients from N data • Upper half is wavelet functions and lower half is coefficient values N/2 N N/2

9. Wavelet Transform • Haar Wavelet Transform • Average and differentiate values to get wavelets function and coefficients First half is the average of each pair second half is the Difference of each pair

10. Wavelet Transform • Haar Wavelet Transform • Average and differentiate values to get wavelets function and coefficients First half is the average of each pair second half is the Difference of each pair

11. Wavelet Transform • Haar Wavelet Transform • First level decomposition HL HH LL LH

12. Wavelet Transform • Haar Wavelet Transform • Haar matrix can do these steps in one operation

13. Wavelet Transform • D4 Wavelet Transform • Use scaling function • Upper half scaling coefficients and lower half wavelets coefficients

14. Wavelet Transform • D4 Wavelet Transform • D4 use four scaling function to transform image Scaling functions Wavelet functions

15. Features Extraction • Feature Vector • F-norm extract the image features from scaled image matrix

16. Features Extraction • Feature Vector • F-norm extract the image features from scaled image matrix ||A0||F ||A1||F ||A3||F ||A5||F ||A7||F ||A0||F=0; ||A1||F =(5762+7042+7042+6402)1/2 ∆A1= ||A1||F - ||A0||F =1316.29 ||A2||F ||A4||F ||A6||F Feature vector : VAF={∆A1, ∆A2, ∆A3, ∆A4……. ∆An)

17. Similarity Criteria • Image matching criteria • Feature vector is calculate both for query image and indexed image • Extracts similarity criteria from feature vector Similarity αiof ∆Ai and∆Bi Image A Similarity αiof full two images Image B

18. Progressive Retrieval • Rough Filtering from LL coefficient • Calculate Standard variances vectors • Query image as(σrq , σgq , σbq ) & database image as(σrd , σgd , σbd ) • Roughly filter out database image using • F=(βσrq < σrq < σrq / β) && (βσgq < σgq < σgq / β) && (βσbq < σbq < σbq / β) where βε (0,1) • If F is false then image is not any kind of similar • Progressive Rough Filtering • Filter considering the high frequency component with LH and HL coefficients • More precise filtering • LL coefficient best reflect the image feature • Apply similarity criteria to LL coefficient • If α exceeds certain threshold, discard as mismatch • Iteration • Iterate filtering process for all decomposition level to return precise image

19. Experimental Result • Experiment Setup • D4 and Haar wavelet transform to decompose images • Maximal decomposition level =4 • F-norm apply to extract image feature both for indexing and query image • Total 4 groups of images indexed, each containing 600 images • All images are preprocessed to be 256X256 sizes

20. Experimental Result • Query result using Haar Wavelet • Relevant images retrieved using the similarity constants

21. Experimental Result • Query result using D4 Wavelet • Relevant images retrieved using the similarity constants

22. Experimental Result • Recall Rate Comparison • D4 wavelet recall rete is higher than the haar and existing wavelet histogram

23. Experimental Result • Retrieval Speed Comparison • Both D4 and Haar are slower than existing histogram wavelet

24. Conclusion • Proposed CBIR applied • Wavelet decomposition of images • F-norm to extract images features • Progressive retrieval to get the precise result • Proposed CBIR • Retrieve more accurate result than existing wavelet technique • D4 wavelet ensure greater speed with increase recall rate • Achieved high retrieval performance in real time CBIR systems