Content Based Image Retrieval System
2016-08-23
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In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to
achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined
class is labeled according to the selected threshold, giving as a result pixel groups that share visual char-acteristics in the image. Several methods have been proposed in order to solve threshold selection prob-lems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the
1D histogram of a gray level image and whose parameters are calculated using three nature inspired algo-rithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each
Gaussian function approximates the hist
achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined
class is labeled according to the selected threshold, giving as a result pixel groups that share visual char-acteristics in the image. Several methods have been proposed in order to solve threshold selection prob-lems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the
1D histogram of a gray level image and whose parameters are calculated using three nature inspired algo-rithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each
Gaussian function approximates the hist
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