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Hierarchical Distributed Genetic Algorithm for Image Segmentation. Hanchuan Peng, Fuhui Long*, Zheru Chi, and Wanshi Siu. Email: {fhlong, phc, enzheru}@eie.polyu.edu.hk
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Hierarchical Distributed Genetic Algorithm for Image Segmentation Hanchuan Peng, Fuhui Long*, Zheru Chi, and Wanshi Siu Email: {fhlong, phc, enzheru}@eie.polyu.edu.hk Center for Multimedia Signal Processing, Department of Electronic & Information Engineering, The Hong Kong Polytechnic University, Hong Kong
Abstract A new Hierarchical Distributed Genetic Algorithm (HDGA) is proposed for image segmentation. • Histogram dichotomy: to explore the statistical property of input image and produce a hierarchically quantized image. • HDGA is imposed on the quantized image to explore the spatial connectivity and produce final segmentation result. HDGA is a major improvement of the original Distributed Genetic Algorithm (DGA) and Multiscale Distributed Genetic Algorithm (MDGA): • A priori assumption • Chromosome structure • Fitness function • Genetic operations Our experiments prove the advantages of HDGA.
Outline • Introduction • Details of HDGA • Experimental Results • Discussion & Conclusion
Introduction: Paradigms for Image Segmentation • A lot of existing algorithms for image segmentation. • Gray-level thresholding of local/global/deterministic/fuzzy/stochastic schemes • Iterative pixel classification (including deterministic and stochastic relaxation) • Parameter space clustering (including probabilistic and fuzzy clustering) • Surface fitting, surface classification and surface/region growing • Edge detection • Statistical models (including Markov Random Field (MRF), Gibbs random field, etc) • Neural networks • Genetic Algorithm (GA)
Introduction: Genetic Algorithms for Image Segmentation • Haseyama’s GA: Minimizing an MSE function for segmentation • Bhanu’s GA: Hybrid model and parameter optimization • Bhandarkar’s GA: Region adjacency graph generation & cost function minimization • Kim’s hybrid model of GA & MRF • Horita’s GA: Region segmentation of K-mean clustering • Scheunders’s genetic Lloyd-Max Quantizer (LMQ) • Andrey’s "distributed" GA based on classifier system • Long’s multilevel distributed genetic algorithm • ……
Introduction: Genetic Approaches for Image Segmentation • Use GA as an alternative optimization method of traditional image segmentation techniques. • Use GA to remove the sensitivity of the present image segmentation techniques to the initial conditions. Based on existing segmentation techniques • Use GA in a more novel and promising way, which codes the segmentation process model itself, instead of the model parameters. New approach!
Introduction: DGA (Distributed Genetic Algorithm) • DGA is novel because it is not based on existing segmentation techniques • distributed GA • classifier system • “Distributed”: the genetic operations, i.e. selection, crossover, mutation, are performed on locally distributed subgroups of chromosomes, but not globally on all chromosomes in the whole population. • Classifier system: a set of symbolic production rules. A classifier is a condition/action rule. It exchanges message with environment through detectors and effectors.
Introduction: DGA – Paradigm • Image segmentation: a function that takes an image as input and a labeled image as output. • The function is represented by classifier system, which consists of a set of spatially organized binary-coded production rules imposed on each pixel. • By iteratively modifying the production rules using a distributed genetic algorithm, the rule set encoding the possibly best segmentation can be obtained.
Introduction: DGA – Main Problems • predefine region numbers on the feature histogram • unreasonable initialization scheme of chromosome population • redundant and inefficient condition-action chromosome structure
Details of HDGA: HDGA – A Major Improvement of DGA • a new unsupervised image segmentation method based on: • hierarchical adaptive thresholding (HAT) • distributed GA
Details of HDGA: Role of HAT • HAT explores the statistical property of the input image • provide a reasonable initialization for GA operations • progressive segmentation
Details of HDGA: Role of Distributed GA • Distributed genetic algorithm explores the spatial connectivity • New chromosome structure • New fitness function • New genetic operations
Details of HDGA: Main Advantages of Our Model • It outperforms Andrey's DGA model: • adaptively and effectively controls the segmentation quality without a priori assumption of the image region number; • produce regions with high homogeneity, high contrast, low noise, and accurate boundaries; • more efficient in both computation and storage.
Details of HDGA: Paradigm of HAT The image feature histogram is repeatedly dichotomized into hierarchical continuous intervals until each of the intervals has a pixel-by-pixel MSE less than a given positive threshold TMSE We can prove: the sum of the pixel variances on all intervals in a higher level is always smaller than that in the lower level --- progressive segmentation
GA initialization in our model GA initialization in Andrey’s model Details of HDGA: HAT based Initialization
Details of HDGA: Distributed GA-based Segmentation 1. HAT based initialization- DLI 2. Evaluation by Fitness Function 3. Genetic Operations 3.1 Selection--- select the cp,q with the largest fitness fp,q in m,n 3.2 Crossover-- produce new offspring 3.3 Mutation – replace cm,n with any chromosome in the whole population randomly according to probability rm 4. Repeat 2, 3 until stop criterion is satisfied
Progressive Segmentation on Different Levels for "bird" Level 1 Level 2 Level 3 Level 4
Segmentation: HDGA vs DGA for “bird” HDGA DGA
Segmentation: HDGA vs DGA for “lena” HDGA DGA
Segmentation: HDGA vs DGA for “peppers” HDGA DGA
Quantitative Evaluation • Region Homogeneity – H • Region Contrast – C • Region boundary accuracy – rA • Number of regions – NR • Speed • convergence speed • computational complexity • Storage complexity Note: For 1,2,3, the larger the better; For 4,5,6, the smaller the better.
Region Homogeneity where Region Contrast where Region Boundary Accuracy
Conclusions • HAT explores the statistical property of the input image • provide a reasonable initialization for GA operations • progressive segmentation • Distributed genetic algorithm explores the spatial connectivity • new chromosome structure, fitness function, genetic operations • Our new model outperforms Andrey et al's DGA model • adaptively and effectively controls the segmentation quality • without a priori assumption of the image region number; • produce regions with high homogeneity, high contrast, low noise, and accurate boundaries; • more efficient in both computation and storage.