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

2. Mobile Pervasive and Sensor Systems Laboratory Table of Contents  Introduction  Target Environment  Proposed CBIR  Wavelet Transform  Feature Extraction  Similarity Criteria  Progressive Retrieval Strategy  Experiment Result  Conclusion 1

3. Mobile Pervasive and Sensor Systems Laboratory Introduction  Content Based Image Retrieval  Database is huge  Retrieved the desired image from the database 2

4. Mobile Pervasive and Sensor Systems Laboratory Introduction  Content Based Image Retrieval  Images have specific features-horizontal or vertical lines  Image features are compared to find similar images 3 Query image Database Image Feature extract to compare

5. Mobile Pervasive and Sensor Systems Laboratory 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 4

6. Mobile Pervasive and Sensor Systems Laboratory Proposed CBIR  Wavelet Based CBIR  Indexing -wavelet decomposition then F-norm  Searching-wavelet decomposition, F-norm then similarity matching 5 Searching Process Searching Process Indexing Process Indexing Process

7. Mobile Pervasive and Sensor Systems Laboratory Wavelet Transform 6  Wavelet Transformation  Decompose using rescaling and keeping details of image

8. Mobile Pervasive and Sensor Systems Laboratory Wavelet Transform 7  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 N/2

9. Mobile Pervasive and Sensor Systems Laboratory Wavelet Transform 8  Haar Wavelet Transform  Average and differentiate values to get wavelets function and coefficients 57670411521280134414721536 704640115610881344140815361600 76883212161472 15361600 832 9601344153616001536 832 9601216153616001536 960896 10881600 1536 768 832 128014721600 448768704640128014081600 640121614081536-64-128 0 6721122137615683268-64 800134415041600-32-256-640 8321152156815360-384-640 8321088156815360-256-640 9289921600156832-192064 7688321376160000-1920 60867213441600-16064-1280 First half is the average of each pair First half is the average of each pair second half is the Difference of each pair second half is the Difference of each pair

10. Mobile Pervasive and Sensor Systems Laboratory Wavelet Transform 9  Haar Wavelet Transform  Average and differentiate values to get wavelets function and coefficients 640121614081536-64-128 0 6721122137615683268-64 800134415041600-32-256-640 8321152156815360-384-640 8321088156815360-256-640 9289921600156832-192064 7688321376160000-1920 60867213441600-16064-1280 First half is the average of each pair First half is the average of each pair second half is the Difference of each pair second half is the Difference of each pair 656116913921552-16-30-96-32 816124815361568-16-320-640 88010401584155216-224-3232 68875213601600-8032-1600 -164716-16-48-98-3232 -1696-3232-166400 -4848-16 -32 80 16080-32 0

11. Mobile Pervasive and Sensor Systems Laboratory Wavelet Transform 10  Haar Wavelet Transform  First level decomposition 57670411521280134414721536 704640115610881344140815361600 76883212161472 15361600 832 9601344153616001536 832 9601216153616001536 960896 10881600 1536 768 832 128014721600 448768704640128014081600 656116913921552-16-30-96-32 816124815361568-16-320-640 88010401584155216-224-3232 68875213601600-8032-1600 -164716-16-48-98-3232 -1696-3232-166400 -4848-16 -32 80 16080-32 0 LL LH HL HH

12. Mobile Pervasive and Sensor Systems Laboratory Wavelet Transform 11  Haar Wavelet Transform  Haar matrix can do these steps in one operation

13. Mobile Pervasive and Sensor Systems Laboratory Wavelet Transform 12  D4 Wavelet Transform  Use scaling function  Upper half scaling coefficients and lower half wavelets coefficients

14. Mobile Pervasive and Sensor Systems Laboratory Wavelet Transform 13  D4 Wavelet Transform  D4 use four scaling function to transform image Scaling functions Wavelet functions

15. Mobile Pervasive and Sensor Systems Laboratory Features Extraction 14  Feature Vector  F-norm extract the image features from scaled image matrix

16. Mobile Pervasive and Sensor Systems Laboratory Features Extraction 15  Feature Vector  F-norm extract the image features from scaled image matrix 57670411521280134414721536 704640115610881344140815361600 76883212161472 15361600 832 9601344153616001536 832 9601216153616001536 960896 10881600 1536 768 832 128014721600 448768704640128014081600 ||A 0 || F ||A 0 || F =0; ||A 1 || F =(576 2 +704 2 +704 2 +640 2 ) 1/2 ∆A 1 = ||A 1 || F - ||A 0 || F =1316.29 ||A 0 || F =0; ||A 1 || F =(576 2 +704 2 +704 2 +640 2 ) 1/2 ∆A 1 = ||A 1 || F - ||A 0 || F =1316.29 ||A 1 || F ||A 2 || F ||A 7 || F ||A 3 || F ||A 4 || F ||A 5 || F ||A 6 || F Feature vector : V AF ={∆A 1, ∆A 2, ∆A 3, ∆A 4……. ∆A n ) Feature vector : V AF ={∆A 1, ∆A 2, ∆A 3, ∆A 4……. ∆A n )

17. Mobile Pervasive and Sensor Systems Laboratory Similarity Criteria 16  Image matching criteria  Feature vector is calculate both for query image and indexed image  Extracts similarity criteria from feature vector Similarity α i of ∆A i and ∆B i Image A Image B Similarity α i of full two images

18. Mobile Pervasive and Sensor Systems Laboratory Progressive Retrieval 17  Rough Filtering from LL coefficient  Calculate Standard variances vectors  Query image as(σ r q, σ g q, σ b q ) & database image as(σ r d, σ g d, σ b d )  Roughly filter out database image using  F=(βσ r q < σ r q < σ r q / β) && (βσ g q < σ g q < σ g q / β) && (βσ b q < σ b q < σ b q / β) where β ε (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. Mobile Pervasive and Sensor Systems Laboratory Experimental Result 18  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. Mobile Pervasive and Sensor Systems Laboratory Experimental Result 19  Query result using Haar Wavelet  Relevant images retrieved using the similarity constants

21. Mobile Pervasive and Sensor Systems Laboratory Experimental Result 20  Query result using D4 Wavelet  Relevant images retrieved using the similarity constants

22. Mobile Pervasive and Sensor Systems Laboratory Experimental Result 21  Recall Rate Comparison  D4 wavelet recall rete is higher than the haar and existing wavelet histogram

23. Mobile Pervasive and Sensor Systems Laboratory Experimental Result 22  Retrieval Speed Comparison  Both D4 and Haar are slower than existing histogram wavelet

24. Mobile Pervasive and Sensor Systems Laboratory Conclusion 23  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

25. Mobile Pervasive and Sensor Systems Laboratory 24

26. Mobile Pervasive and Sensor Systems Laboratory 25