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Splitting and Merging Approach for Image Indexing and Retrieval in DC Domain

Splitting and Merging Approach for Image Indexing and Retrieval in DC Domain. By :S. Irianto. Supervisor Prof. Jianmin Jiang. Department of EIMC School of Informatics. MOTIVATION. a growing on demand for image database applications more people have accessed to large databases

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Splitting and Merging Approach for Image Indexing and Retrieval in DC Domain

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  1. Splitting and Merging Approach for Image Indexing and Retrieval in DC Domain By :S. Irianto Supervisor Prof. Jianmin Jiang Department of EIMC School of Informatics

  2. MOTIVATION • a growing on demand for image database applications • more people have accessed to large databases • the research community to develop methods to archive, query and retrieve this database based on their content.

  3. THE AIMS • TO PRESENT AN EFFECTIVE IMAGE RETRIEVAL ON NON-DCT IMAGES • TO OFFER AN ALTERNATIVE METHOD OF IMAGE RETRIEVAL • TO INTRODUCE SEGMENTATION BASED FOR IMAGE RTREIVAL

  4. RELATED WORKS • VIRS (Gupta,1997) • QBIC - wwwqbic.almaden.ibm.com • Photobook-vismod.media.mit.edu • Virage – www.virage.com • Candid - public.lanl.gov/kelly/CANDID/method.shtml

  5. RELATED WORKS …… • RetrievalWare – www.excalib.com • VisualSEEK and WebSEEK –WWW.columbia.edu • Netra – vivaldi.ece.ecsb.edu/Netra/ • MARS –jadzia.ifp.uiuc.edu:8000

  6. DC IMAGE REPRESENTATION • Instead of working on the full image very time consuming and complex. • we extract only one value on every block of the jpeg images.

  7. DC IMAGE ……….. • DC image extracted basically consist of average of other 63 pixels

  8. DC IMAGE ……. >>3 + 128 DC image 8 X 8 DCT image

  9. DC AND SEGMENTED IMAGE - EXAMPLES Grayscale RGB DC image Segmented

  10. DC vs Segmented images Segmented image DC image

  11. DC vs ………………….. DC image Segmented image

  12. SPLITTING AND MERGING(adopted from Dubuisson1993) • First, we must split the image, start by considering the entire image as one region • If the entire region is coherent or has sufficient similarity, leave it unmodified

  13. SPLITTING …………. • If the region is not sufficiently coherent, split it into four quadrants; and recursively apply these steps to each new region.

  14. ALGORITHM split phase: create an (empty) tree T with regions in its nodes createa node for T (which is root and the only leave) put X in this node for all leaves L of T take region RL from L if RL is sufficiently homogenous thenreturn RL in L if RL is not sufficiently homogenous then separate RL into four sub-regions R1L, R2L, R3L, R4L, of equal size create four new leaves L1; L2; L3; L4 as sons of L putRiL in L1; 1 ≤ i ≤ 4

  15. ALGORITHM …………….. merge phase: create an (empty) set S of (empty) trees for all leaves L of T add L as a new tree to S (with L as root and only leave) forany root L in S forany root L’ ≠ L in S ifthe segments RL in L and RL’ in L’ are neighboured and similar thencreate a new root L in S with sons L and L’ putthe segment RL Ư RL’ into L’

  16. SPLITTING AND MERGING PROCESS First split

  17. SPLITTING AND MERGING PROCESS Second split merge

  18. IMAGE QUERY • user make a query an image, once a query is specified, • we score each image in the database on how closely it satisfies the query

  19. IMAGE QUERY ……. • The score for each atomic query (DC -image) is calculated by using Euclidean distance transform

  20. RESULTS ANALYSIS • Constraint • 1,000 images consist of seven classes consists of “motorbike”, “building”, “car”, “cat”, “flower”, “mountain”, and “sky”. • images of size 2 to 10 K

  21. RESULTS ……….. • (DC image) -our propose method • Highest precision at 0.89 for MOUNTAIN • Lowest precision at 0.22 for CAT • DCT image • highest precision at 0.65 for MOUNTAIN • Lowest precision at 0.45 for CAT

  22. RESULTS ……….. • Average precision • for DC image : 0.59 • for DCT image : 0.52

  23. Precision and recall

  24. Precision and recall…………...

  25. Number of regions vs precision

  26. Number of regions …………….

  27. CONCLUDING REMARK • SaM approach can be used as an alternative technique to improve the effectiveness of image retrieval, particularly for non full DCT based images • The evidence seems indicate that the split-merge on DC image approach demonstrates somewhat higher on precision than RGB approach as existing technique, even though, the precision is not significantly different

  28. FUTURE WORKS • WE ARE SETTING UP SEGMENTATION ON DC IMAGES DIRECTLY FROM RGB IMAGE BY USING REGION GROWING AND SPLIT-MERGE SEGMENTATION

  29. Thank you for your patient Wish you enjoy

  30. Notes • Homogenity measured by • Similarity (merge) measured by

  31. Notes ………. • Homogenity measured by • Merge measured by

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