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

S. A. Bakar, M. S. Hitam, W. Yussof
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引用次数: 39

Abstract

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.
基于内容的图像检索使用SIFT对二值和灰度图像
本文提出了一种基于内容的图像检索(CBIR)的替代方法,使用尺度不变特征变换(SIFT)算法对二值和灰度图像进行检索。将SIFT算法用于CBIR的动机是由于SIFT对尺度、旋转和平移不变性,以及对仿射畸变和光照变化的部分不变性。受这些事实的启发,本文利用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中的图像之间进行匹配。实验结果表明,采用SIFT算法的CBIR对多角图像的检索效果优于对少角图像的检索效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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