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Object Based Image Retrieval from Database Using Combined Features

机译:基于对象的图像从数据库中检索使用组合功能

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Content based image retrieval (CBIR) is a promising way to address image retrieval based on the visual features of an image like color, texture and shape. Every visual feature will address a specific property of the image, so the state of the art focuses on combination of multiple visual features for content based image retrieval. In this paper we have devised a content based image retrieval system based on the combination of local and global features. The local features used are Bidirectional Empirical Mode Decomposition (BEMD) technique for edge detection and Harris corner detector to detect the corner points of an image. The global feature used is HSV colorfeature. For the experimental purpose the COIL-100 database has been used. The result show significant improvement in the retrieval accuracy when compared to the existing systems.
机译:基于内容的图像检索(CBIR)是基于图像的视觉特征来解决图像检索的有希望的方式,如颜色,纹理和形状。每个可视特征都将解决图像的特定属性,因此本领域的状态侧重于基于内容的图像检索的多个视觉特征的组合。在本文中,我们根据本地和全局特征的组合设计了基于内容的图像检索系统。所使用的本地特征是边缘检测和哈里斯角探测器的双向经验模式分解(BEMD)技术,以检测图像的角点。使用的全局功能是HSV ColorFeature。对于实验目的,使用了线圈-100数据库。结果显示与现有系统相比的检索精度显着提高。

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