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

Splitting and Merging Approach for Image Splitting and Merging Approach for Image

Indexing and Retrieval in DC DomainIndexing and Retrieval in DC Domain By :S. Irianto

SupervisorProf. Jianmin Jiang

Department of EIMCSchool of Informatics

MOTIVATION MOTIVATION a growing on demand for

image database applicationsmore people have accessed to

large databases the research community to

develop methods to archive, query and retrieve this database based on their content.

THE AIMSTHE 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

RELATED WORKSRELATED WORKS

VIRS (Gupta,1997)QBIC - wwwqbic.almaden.ibm.com

Photobook- vismod.media.mit.edu

Virage – www.virage.comCandid -

public.lanl.gov/kelly/CANDID/method.shtml

RELATED WORKSRELATED WORKS …… ……

RetrievalWare – www.excalib.comVisualSEEK and WebSEEK – WWW.columbia.edu

Netra – vivaldi.ece.ecsb.edu/Netra/MARS – jadzia.ifp.uiuc.edu:8000

DC IMAGE REPRESENTATIONDC 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

0

7

0

),(8

1 ),(

i j

jcirfcrDC

DC IMAGE ………..DC IMAGE ………..

DC image extracted basically consist of average of other 63 pixels

DC DC DC DC DC DC DC DC








DC AC AC AC AC AC AC AC

AC AC AC AC AC AC AC AC






















AC AC AC AC AC AC AC ACDC image

>>3 + 128

DC IMAGE …….

8 X 8 DCT image

RGB Grayscale DC image Segmented

DC AND SEGMENTED IMAGE - EXAMPLES

DC vs Segmented imagesDC vs Segmented images

DC image Segmented image

DC vs …………………..DC vs …………………..

DC image Segmented image

SPLITTING AND MERGINGSPLITTING AND MERGING(adopted from Dubuisson1993)(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

SPLITTING ………….SPLITTING ………….

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

ALGORITHMALGORITHMsplit phase: create an (empty) tree T with regions in its nodescreate a 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 then return RL in L if RL is not sufficiently homogenous then separate RL into four sub-regions R1

L,

R2L, R

3L, R

4L, of equal size

create four new leaves L1; L2; L3; L4 as sons of L put Ri L in L1; 1 ≤ i ≤ 4

ALGORITHM ……………..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) for any root L in S for any root L’ ≠ L in S if the segments RL in L and RL’ in L’ are neighboured and similar then create a new root L in S with sons L and L’

put the segment RL Ư RL’ into L’

I1 I2

I3 I4

DC image(I)

First split

SPLITTING AND MERGING PROCESS

I1 I2

I3 I41 I42

I43 I44

Second split

I1 I2

I3 I41 I42

I43 merge

SPLITTING AND MERGING PROCESS

IMAGE QUERYIMAGE 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

IMAGE QUERY …….IMAGE QUERY …….

The score for each atomic query (DC -image) is calculated by using Euclidean distance transform

RESULTS ANALYSISRESULTS ANALYSIS

Constraint1,000 images consist of seven classes consists of “motorbike”, “building”, “car”, “cat”, “flower”, “mountain”, and “sky”.

images of size 2 to 10 K

RESULTS ………..RESULTS ………..

(DC image) -our propose methodHighest precision at 0.89 for MOUNTAINLowest precision at 0.22 for CAT

DCT imagehighest precision at 0.65 for MOUNTAINLowest precision at 0.45 for CAT

RESULTS ………..RESULTS ………..

Average precisionfor DC image : 0.59for DCT image : 0.52

Precision and recallPrecision and recall

Image retrieval effectiveness

0

0.2

0.4

0.6

0.8

1

0 0.02 0.04 0.06 0.08 0.1

Recall

Pre

cis

ion SaM image

RGB image

Precision and recall…………...Precision and recall…………...Image retrieval effectiveness

mountain

cat

sky

building

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.02 0.04 0.06 0.08 0.1

Recall

Pre

cis

ion SaM image

RGB image

Number of regions vs precisionNumber of regions vs precision

Precision and its number of regions

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 50 100 150 200 250 300 350 400

Number of regions

Prec

isio

n

precision

Linear (precision)

Number of regions …………….Number of regions …………….

Precision and its number of regions

mountain

cat

sky

building

bike

carflow er

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 50 100 150 200 250 300 350 400

Number of regions

Prec

isio

n

precision

Linear (precision)

CONCLUDING REMARKCONCLUDING REMARKSaM 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

FUTURE WORKSFUTURE WORKS

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

Thank you for your patientWish you enjoy

NotesNotes

Homogenity measured by

Similarity (merge) measured by

Notes ……….Notes ……….

Homogenity measured by

(10) Threshold 2

Merge measured by

(10) Threshold - 21

Documents

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