Saliency-directed color image segmentation using modified particle swarm optimization
Autor: | Jin-Jang Leou, Chi-Yu Lee, Han-Hui Hsiao |
---|---|
Rok vydání: | 2012 |
Předmět: |
Color histogram
Computer science Color normalization Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Image texture Computer vision Saliency map Electrical and Electronic Engineering Image gradient Demosaicing Segmentation-based object categorization business.industry Color image Binary image Pattern recognition Image segmentation Color quantization Control and Systems Engineering Region growing Computer Science::Computer Vision and Pattern Recognition Signal Processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
Zdroj: | Signal Processing. 92:1-18 |
ISSN: | 0165-1684 |
DOI: | 10.1016/j.sigpro.2011.04.026 |
Popis: | Color image segmentation, an ill-posed problem, can be treated as a process of dividing a color image into some constituent regions and each region is homogeneous. In this study, a saliency-directed color image segmentation approach using ''simple'' modified particle swarm optimization (PSO) is proposed, in which both low-level features and high-level image semantics extracted from each color image are employed. To extract high-level image semantics from each color image, the visual attention saliency map for each color image is generated by three (color, intensity, and orientation) feature maps, which is used to guide region merging using ''simple'' modified PSO and a hybrid fitness function for color image segmentation. The proposed approach contains four stages, namely, color quantization, feature extraction, small region elimination, and region merging using ''simple'' modified PSO. Based on the experimental results obtained in this study, as compared with four comparison approaches, the proposed approach usually provides the better color image segmentation results. |
Databáze: | OpenAIRE |
Externí odkaz: |