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Content Based Color Image Retrieval vi Wavelet TransformationsPowerPoint Presentation

Content Based Color Image Retrieval vi Wavelet Transformations

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### 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

Table of Contents

- Introduction
- Target Environment
- Proposed CBIR
- Wavelet Transform
- Feature Extraction
- Similarity Criteria
- Progressive Retrieval Strategy
- Experiment Result
- Conclusion

Introduction

- Content Based Image Retrieval
- Database is huge
- Retrieved the desired image from the database

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

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

- Based on Object Visual contents of image

Proposed CBIR

- Wavelet Based CBIR
- Indexing -wavelet decomposition then F-norm
- Searching-wavelet decomposition, F-norm then similarity matching

Searching

Process

Indexing

Process

Wavelet Transform

- Wavelet Transformation
- Decompose using rescaling and keeping details of image

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

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

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

Wavelet Transform

- Haar Wavelet Transform
- Haar matrix can do these steps in one operation

Wavelet Transform

- D4 Wavelet Transform
- Use scaling function
- Upper half scaling coefficients and lower half wavelets coefficients

Wavelet Transform

- D4 Wavelet Transform
- D4 use four scaling function to transform image

Scaling functions

Wavelet functions

Features Extraction

- Feature Vector
- F-norm extract the image features from scaled image matrix

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)

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

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

- Calculate Standard variances vectors
- 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

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

Experimental Result

- Query result using Haar Wavelet
- Relevant images retrieved using the similarity constants

Experimental Result

- Query result using D4 Wavelet
- Relevant images retrieved using the similarity constants

Experimental Result

- Recall Rate Comparison
- D4 wavelet recall rete is higher than the haar and existing wavelet histogram

Experimental Result

- Retrieval Speed Comparison
- Both D4 and Haar are slower than existing histogram wavelet

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

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