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首页> 外文期刊>Journal of medical systems >Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform
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Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform

机译:基于内容的图像检索通过使用颜色描述符和离散小波变换

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摘要

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.
机译:由于近期技术的发展,多媒体的复杂性显着增加,类似的多媒体内容的检索是一个开放的研究问题。基于内容的图像检索(CBIR)是提供用于图像搜索的框架的过程,并且通常用于从图像数据库检索图像的低级视觉特征。任何图像检索过程的基本要求是在视觉外观中具有近似相似性的图像。颜色,形状和纹理是低级图像特征的示例。该功能在图像处理中发挥着重要作用。图像的强大表示被称为特征向量,并且应用特征提取技术来获取在分类和识别图像的分类和识别中的特征。随着特征定义图像的行为,它们在储存方面展示了它的位置,分类效率,显然也在时间消耗。在本文中,我们将讨论各种类型的特征,特征提取技术,并在哪些场景中解释,特征提取技术将更好。 CBIR方法的有效性基本上基于特征提取。在图像处理符号中,如对象识别和图像检索特征描述符是最重要的步骤中的巨大。 CBIR的主要思想是,通过使用距离指标,它可以将相关图像搜索到作为查询的图像传递的图像。用Cany边缘直方图和离散小波变换在YCBCR颜色构建的图像检索解释了所提出的方法。直方图和离散小波变换的边缘的组合增加了基于内容的搜索图像检索框架的性能。不同小波的执行另外对比发现特定小波工作的适用性进行图像检索。准备了所提出的算法并试图为Wang Image数据库实现。对于图像检索目的,使用人工神经网络(ANN)并应用于CBIR域中的标准数据集。通过计算精度和召回值并与不同的其他方法进行比较来评估推荐描述符的执行,以证明我们的方法的主要方法。所提出的方法的效率和有效性优于平均精度和召回值期间现有的研究。

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