Liao huilian szu ti dsps lab aug 27 2007
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Optimizer based on particle swarm optimization and LBG (PSO-LBG) — application in vector quantization. Liao Huilian SZU TI-DSPs LAB Aug 27, 2007. School of Software Engineering, Shenzhen University. Outline. Vector quantization (VQ) LBG Particle swarm optimization (PSO)

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Liao Huilian SZU TI-DSPs LAB Aug 27, 2007

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Liao huilian szu ti dsps lab aug 27 2007

Optimizer based on particle swarm optimization and LBG (PSO-LBG)

— application in vector quantization

Liao Huilian

SZU TI-DSPs LAB

Aug 27, 2007

School of Software Engineering, Shenzhen University


Outline
Outline (PSO-LBG)

  • Vector quantization (VQ)

    • LBG

  • Particle swarm optimization (PSO)

  • Optimizer based on PSO and LBG (PSO-LBG)

    • PSO-LBG

    • 2-dimensional simulation

    • Performance comparison

  • Conclusion

  • Acknowledgement

School of Software Engineering, Shenzhen University


Outline1
Outline (PSO-LBG)

  • Vector quantization (VQ)

    • LBG

  • Particle swarm optimization (PSO)

  • Optimizer based on PSO and LBG (PSO-LBG)

    • PSO-LBG

    • 2-dimensional simulation

    • Performance comparison

  • Conclusion

  • Acknowledgement

School of Software Engineering, Shenzhen University


Vector quantization vq
Vector quantization (VQ) (PSO-LBG)

School of Software Engineering, Shenzhen University


Liao huilian szu ti dsps lab aug 27 2007
LBG (PSO-LBG)

  • LBG, a well-known method of VQ, was proposed by Linde, Buzo and Gray in 1980

  • Apply two optimality criteria iteratively:

    • Nearest neighbour criterion during assigning training vectors

    • Centroid criterion during updating codewords (code vectors)

  • Drawbacks:

    • Local optimization

    • Sensitive to the selection of initial codebook

School of Software Engineering, Shenzhen University


Liao huilian szu ti dsps lab aug 27 2007
LBG (PSO-LBG)

School of Software Engineering, Shenzhen University


Liao huilian szu ti dsps lab aug 27 2007
LBG (PSO-LBG)

School of Software Engineering, Shenzhen University


Outline2
Outline (PSO-LBG)

  • Vector quantization (VQ)

    • LBG

  • Particle swarm optimization (PSO)

  • Optimizer based on PSO and LBG (PSO-LBG)

    • PSO-LBG

    • 2-dimensional simulation

    • Performance comparison

  • Conclusion

  • Acknowledgement

School of Software Engineering, Shenzhen University


Particle swarm optimization
Particle swarm optimization (PSO-LBG)

  • PSO was proposed by Eberhart and Kennedy in 1995

  • Advantages:

    • Simplicity of implementation

    • Few parameters

    • High convergence rate

  • Population based optimization

    • Remember the best location of itself (Pbest)

    • Remember the best experience in the swarm (Gbest)

School of Software Engineering, Shenzhen University


Particle swarm optimization1
Particle swarm optimization (PSO-LBG)

School of Software Engineering, Shenzhen University


Outline3
Outline (PSO-LBG)

  • Vector quantization (VQ)

    • LBG

  • Particle swarm optimization (PSO)

  • Optimizer based on PSO and LBG (PSO-LBG)

    • PSO-LBG

    • 2-dimensional simulation

    • Performance comparison

  • Conclusion

  • Acknowledgement

School of Software Engineering, Shenzhen University


Pso lbg
PSO-LBG (PSO-LBG)

  • Based on conventional PSO and LBG algorithms

  • PSO-LBG

    • Structure of particle

    • Particle-pair model

    • Updating process

  • Apply in Vector Quantization (VQ)

School of Software Engineering, Shenzhen University


Structure of particle
Structure of particle (PSO-LBG)

School of Software Engineering, Shenzhen University


Updating model
Updating model (PSO-LBG)

School of Software Engineering, Shenzhen University


Updating process
Updating process (PSO-LBG)

  • PSO-LBG performs three steps at each iteration:

    • Step1: Basic PSO operations

    • Step2: Classical vector quantizer, i.e. LBG algorithm

    • Step3: Deal with codewords “flying” over the boundary of training vector space

School of Software Engineering, Shenzhen University


Step1 basic pso operations
Step1 (PSO-LBG)-Basic PSO operations

  • Difference between PSO-LBG and PSO

    • Velocity updating: (additive inertia weight )

  • The parameters, and are much smaller than general PSO-based algorithms

  • Apply a particle-pair instead of a large number of particles

School of Software Engineering, Shenzhen University


Why small parameters
Why small parameters? (PSO-LBG)

  • One point larger parameters

  • The solution of PSO-LBG represents N points in the training vector space

School of Software Engineering, Shenzhen University


Why just two particles
Why just two particles? (PSO-LBG)

  • Three particles consisting of two codewords: P1={y1, y2}; P2={y2, y1} and P3={y3, y4}. P3 has a poorer performance

  • During the following iterations, particle P1andP2 are comparative

  • The fly direction of particle P3 is uncertain

School of Software Engineering, Shenzhen University


Why just two particles1
Why just two particles? (PSO-LBG)

Stable convergence Unstable convergence

School of Software Engineering, Shenzhen University


Updating steps 2 3
Updating steps 2 & 3 (PSO-LBG)

  • Apply LBG with only 3 iterations to avoid converging early

  • Deal with the codewords “flying” over the boundary of search space: Replace this kind of codeword with the training vector that has higher distortion

School of Software Engineering, Shenzhen University


Demonstration in 2 dimensional space
Demonstration in (PSO-LBG)2-dimensional space

  • Three objectives PSO-LBG intends to achieve:

    • Disperse codewords

    • Move towards global optimum codebook

    • Codewords are settled reasonably both in high density regions and low density areas of training vectors space

School of Software Engineering, Shenzhen University


Demonstration in 2 dimensional space1
Demonstration in (PSO-LBG)2-dimensional space

LBG

=61.26

Initial codebook

=561.15

PSO-LBG

=46.85

School of Software Engineering, Shenzhen University


Performance comparison
Performance comparison (PSO-LBG)

  • Performance is evaluated by and PSNR

    • : Mean square error between the training vectors and corresponding nearest codewords

    • PSNR: Peak signal to noise ratio

School of Software Engineering, Shenzhen University


Performance comparison1
Performance comparison (PSO-LBG)

  • Comparison is conducted among:

    • LBG

    • Fuzzy k-means (FKM)

    • Fuzzy reinforced learning vector quantization (FRLVQ)

    • FRLVQ-FVQ: Apply FRLVQ as the pre-process of FVQ

    • PSO-LBG

School of Software Engineering, Shenzhen University


Experimental images
Experimental images (PSO-LBG)

Pepper

Kgirl

Lena

Cameraman

School of Software Engineering, Shenzhen University


Psnr comparison on lena
PSNR comparison on Lena (PSO-LBG)

School of Software Engineering, Shenzhen University


Convergence comparison on lena
Convergence comparison on Lena (PSO-LBG)

School of Software Engineering, Shenzhen University


Computation time on lena
Computation time on Lena (PSO-LBG)

School of Software Engineering, Shenzhen University


Codebook characteristic on lena
Codebook characteristic on Lena (PSO-LBG)

School of Software Engineering, Shenzhen University


Psnr comparison on pepper
PSNR comparison on pepper (PSO-LBG)

School of Software Engineering, Shenzhen University


Psnr comparison on cameraman
PSNR comparison on cameraman (PSO-LBG)

School of Software Engineering, Shenzhen University


Psnr comparison on kgirl
PSNR comparison on Kgirl (PSO-LBG)

School of Software Engineering, Shenzhen University


Conclusion
Conclusion (PSO-LBG)

  • Experimental results demonstrate that PSO-LBG Outperforms existing algorithms in the field of vector quantization

  • Future work

    • Application in gene clustering

School of Software Engineering, Shenzhen University


Acknowledgement
Acknowledgement (PSO-LBG)

  • My supervisor:

    Prof. Ji

  • All of you

School of Software Engineering, Shenzhen University


Thank you
Thank you! (PSO-LBG)

School of Software Engineering, Shenzhen University


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