Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown.
Over the last years, Support Vector Machines (SVMs) have become a successful approach in classification problems. However, the performance of SVMs is affected harshly by skewed data sets. An SVM learns a biased model that affects the performance of the classifier. Furthermore, SVMs are typically unsuccessful on data sets where the imbalanced ratio is very large. Lately, several techniques have been used to tackle this disadvantage by generating artificial instances. Artificial data instances attempt to add information to the minority class. However, the new instances could introduce noise and decrease the performance of the classifier. In this research, an alternative procedure is suggested, the algorithm finds systematically new instances, improving the performance of SVMs on skewed data sets. The proposed method starts obtaining the support vectors (SVs) from the skewed data set. These initial SVs are used to generate new instances and the PSO algorithm is used to evolve the artificial instances, eliminating noise instances. This research combines the best of optimization and classification techniques. To show the ability of the proposed method to improve the performance of SVMs on skewed data sets, we compare the performance of our method against some classical methods and show that our algorithm outperforms all of them on several data sets.
Abstract. Support Vector Machines (SVM) have shown excellent generalization power in classification problems. However, on skewed data-sets, SVM learns a biased model that affects the classifier performance, which is severely damaged when the unbalanced ratio is very large. In this paper, a new external balancing method for applying SVM on skewed data sets is developed. In the first phase of the method, the separating hyperplane is computed. Support vectors are then used to generate the initial population of PSO algorithm, which is used to improve the population of artificial instances and to eliminate noise instances. Experimental results demonstrate the ability of the proposed method to improve the performance of SVM on imbalanced data-sets.
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