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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.

Big Data has become a very popular term. It refers to the enormous amount of structured, semi-structured and unstructured data that are exponentially generated by high-performance applications in many domains: biochemistry, genetics, molecular biology, physics, astronomy, business, to mention a few. Since the literature of Big Data has increased significantly in recent years, it becomes necessary to develop an overview of the state-of-the-art in Big Data. This paper aims to provide a comprehensive review of Big Data literature of the last 4 years, to identify the main challenges, areas of application, tools and emergent trends of Big Data. To meet this objective, we have analyzed and classified 457 papers concerning Big Data. This review gives relevant B Giner Alor-Hernández

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