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An Effective Visual Descriptor Based on Color and Shape Features for Image Retrieval

机译:基于颜色和形状特征的有效视觉描述符用于图像检索

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In this paper we present a Content-Based Image Retrieval (CBIR) system which extracts color features using Dominant Color Correlogram Descriptor (DCCD) and shape features using Pyramid Histogram of Oriented Gradients (PHOG). The DCCD is a descriptor which extracts global and local color features, whereas the PHOG descriptor extracts spatial information of shape in the image. In order to evaluate the image retrieval effectiveness of the proposed scheme, we used some metrics commonly used in the image retrieval task such as, the Average Retrieval Precision (ARP), the Average Retrieval Rate (ARR) and the Average Normalized Modified Retrieval Rank (ANMRR) and the Average Recall (R)-Average Precision (P) curve. The performance of the proposed algorithm is compared with some other methods which combine more than one visual feature (color, texture, shape). The results show a better performance of the proposed method compared with other methods previously reported in the literature.
机译:在本文中,我们提出了一种基于内容的图像检索(CBIR)系统,该系统使用优势颜色相关图描述符(DCCD)提取颜色特征,并使用定向梯度金字塔直方图(PHOG)提取形状特征。 DCCD是提取全局和局部颜色特征的描述符,而PHOG描述符则提取图像中形状的空间信息。为了评估该方案的图像检索效果,我们使用了图像检索任务中常用的一些指标,例如平均检索精度(ARP),平均检索率(ARR)和平均归一化修改检索等级( ANMRR)和平均召回率(R)-平均精度(P)曲线。将该算法的性能与结合了多个视觉特征(颜色,纹理,形状)的其他一些方法进行了比较。结果表明,与先前文献中报道的其他方法相比,该方法具有更好的性能。

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