Color image quantization using Gaussian Particle Swarm Optimization(CIQ-GPSO)
| Autor: | Dibyendu Barman, Abul Hasnat, Md. Atiqur Rahaman Murshidanad, Suchintya Sarkar |
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| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Gaussian Computer Science::Neural and Evolutionary Computation MathematicsofComputing_NUMERICALANALYSIS ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION k-means clustering Particle swarm optimization 020207 software engineering Pattern recognition 02 engineering and technology Color quantization symbols.namesake ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering symbols Gaussian function 020201 artificial intelligence & image processing Artificial intelligence Multi-swarm optimization Cluster analysis Quantization (image processing) business |
| Zdroj: | 2016 International Conference on Inventive Computation Technologies (ICICT). |
| DOI: | 10.1109/inventive.2016.7823295 |
| Popis: | This article proposes a color image quantization algorithm based on Gaussian Particle Swarm Optimization (GPSO). PSO is a population-based optimization algorithm modeled after the simulation of social behavior of swarms to find near-optimal solutions. The algorithm randomly initializes each particle in the swarm to contain K centroids (i.e. color triplets). The K-means clustering algorithm is then applied on each particle to refine the chosen centroids at user specified probability. Each pixel is assigned to the cluster with the closest centroid. Next the Gaussian PSO is applied to update the centroids obtained using the K-means clustering. For performance analysis the proposed algorithm is tested on standard images in the literature and experimental result shows that the Gaussian PSO based quantization method improves image quality significantly compared to conventional PSO based approach. |
| Databáze: | OpenAIRE |
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