Automatic Detection of Galaxy Type From Datasets of Galaxies Image Based on Image Retrieval Approach.
Automatic Detection of Galaxy Type From Datasets of Galaxies Image Based on Image Retrieval Approach.
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DOI：
10.1038/s41598-017-04605-9
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发表时间：
2017-06-30
影响因子：
4.6
通讯作者：
Xiong S
中科院分区：
文献类型：
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作者：
Abd El Aziz M;Selim IM;Xiong S
本文提出了一种基于图像检索方法从数据集中自动检测星系形态的新方法。目前，提出了几种分类方法来检测图像中的星系类型。然而，在某些情况下，目的不仅是确定查询图像内的星系类型，而且还确定与查询图像最相似的图像。因此，本文提出了一种图像检索方法来检测图像中的星系类型并返回最相似的图像。该方法由两个阶段组成，在第一阶段，基于形状、颜色和纹理描述符提取一组特征，然后二进制正弦余弦算法选择最相关的特征。在第二阶段，计算所查询的星系图像的特征与其他星系图像的特征之间的相似度。我们的实验是使用 EFIGI 目录进行的，该目录包含大约 5000 个不同类型（边缘螺旋、螺旋、椭圆和不规则）的星系图像。我们证明，与粒子群优化（PSO）和遗传算法（GA）方法相比，我们提出的方法具有更好的性能。
This paper presents a new approach for the automatic detection of galaxy morphology from datasets based on an image-retrieval approach. Currently, there are several classification methods proposed to detect galaxy types within an image. However, in some situations, the aim is not only to determine the type of galaxy within the queried image, but also to determine the most similar images for query image. Therefore, this paper proposes an image-retrieval method to detect the type of galaxies within an image and return with the most similar image. The proposed method consists of two stages, in the first stage, a set of features is extracted based on shape, color and texture descriptors, then a binary sine cosine algorithm selects the most relevant features. In the second stage, the similarity between the features of the queried galaxy image and the features of other galaxy images is computed. Our experiments were performed using the EFIGI catalogue, which contains about 5000 galaxies images with different types (edge-on spiral, spiral, elliptical and irregular). We demonstrate that our proposed approach has better performance compared with the particle swarm optimization (PSO) and genetic algorithm (GA) methods.