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Content-Based Image Retrieval using SIFT for binary and greyscale images

机译:使用SIFT对二进制和灰度图像进行基于内容的图像检索

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This paper presents an alternative approach for Content Based Image Retrieval (CBIR) using Scale Invariant Feature Transform (SIFT) algorithm for binary and gray scale images. The motivation to use SIFT algorithm for CBIR is due to the fact that SIFT is invariant to scale, rotation and translation as well as partially invariant to affine distortion and illumination changes. Inspired by these facts, this paper investigates the fundamental properties of SIFT for robust CBIR by using MPEG-7, COIL-20 and ZuBuD image databases. Our approach uses detected keypoints and its descriptors to match between the query images and images from the database. Our experimental results show that the proposed CBIR using SIFT algorithm producing excellent retrieval result for images with many corners as compared to retrieving image with less corners.
机译:本文提出了一种使用基于尺度不变特征变换(SIFT)算法处理基于二进制和灰度图像的基于内容的图像检索(CBIR)的替代方法。将SIFT算法用于CBIR的动机是由于以下事实:SIFT对于缩放,旋转和平移是不变的,并且对于仿射失真和照明变化也部分不变。受这些事实的启发,本文使用MPEG-7,COIL-20和ZuBuD图像数据库研究了SIFT用于鲁棒CBIR的基本特性。我们的方法使用检测到的关键点及其描述符在查询图像和数据库图像之间进行匹配。我们的实验结果表明,与检索较少角点的图像相比,使用SIFT算法提出的CBIR对具有多个角点的图像产生了出色的检索结果。

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