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Color Image Retrieval Using DFT Phase Information

机译:使用DFT相位信息检索彩色图像

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摘要

With the advancement of Image acquisition and storing, image retrieval has been proven as the research problem. Many approaches for image retrieval, has been stated by researchers to solve the image retrieval problem. In this paper we state DFT transform based approach for image retrieval using Image classes. Here formation of Feature vectors of the Images is based on Color based DFT Phase information of images those belongs to same class. DFT Image transform provides effective way to differentiate the image textures. Particularly Phase part of DFT carries the important information about the objects in image. In the proposed approach of image retrieval, DFT phase information is used for representing the images using feature vector effectively. To make image retrieval more accurate, class wise images are considered for creation of database feature vectors. As, images belonging same class are content wise similar, the generalized feature vector is produced for each class Generalized feature vectors represents all images of that class. Cosine correlation similarity measure is used in the proposed approach. 4 Different types of feature vectors are created and tested for each image class. The Images are retrieved based on the feature vector values of DFT Phase information of RGB's planes with similar to that of Feature vector of Image class. Image retrieval Performance of the proposed approach is compared for database of 1000 images of 10 different categories. Average Accuracy of Image retrieval is above 60% for all classes and more than 75% for some of the image classes.
机译:随着图像采集和存储技术的发展,图像检索已成为研究的热点。研究人员已经提出了许多图像检索方法来解决图像检索问题。在本文中,我们阐述了基于DFT变换的图像类检索方法。这里,图像的特征向量的形成是基于属于相同类别的图像的基于颜色的DFT相位信息。 DFT图像变换为区分图像纹理提供了有效的方法。特别是DFT的相位部分承载有关图像中对象的重要信息。在提出的图像检索方法中,DFT相位信息用于有效地使用特征向量表示图像。为了使图像检索更加准确,考虑使用分类图像来创建数据库特征向量。由于属于同一类别的图像在内容方面是相似的,因此针对每个类别生成广义特征向量。广义特征向量表示该类别的所有图像。余弦相关相似度测度在所提出的方法中被使用。为每个图像类别创建和测试4种不同类型的特征向量。基于RGB平面的DFT相位信息的特征向量值来检索图像,类似于Image类的特征向量。图像检索针对10种不同类别的1000张图像的数据库,比较了所提出方法的性能。对于所有类别,图像检索的平均准确度均高于60%,而对于某些图像类别,则超过75%。

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