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基于SIFT,K-Means和LDA的图像检索算法

DOI: 10.13700/j.bh.1001-5965.2013.0601, PP. 1317-1322

Keywords: 尺度不变特征变换(SIFT),K-Means,潜在狄利克雷分布(LDA),基于内容的图像检索,图像匹配

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Abstract:

图像检索一直是信息检索领域的难题。提出了一种基于尺度不变特征变换(SIFT,ScaleInvariantFeatureTransform),K-Means和潜在狄利克雷分布(LDA,LatentDirichletAllocation)的图像检索算法。算法主要分为两个阶段。预备工作得到分类完成的图库、概率分配参数表和基本词库;实现检索是在预备工作的基础上归类测试图片,然后在该类下搜索最相似图片。对比传统的基于文本或内容的检索方法,该算法在检索之前将图片库中所有图片按其本身特征进行自动分类,取代人工标注图像信息的过程,同时由于整个算法完全基于图像特征,故此方法不会引入人工因素的干扰。实验结果表明,该算法能够较为准确地将要检索的图片归为图片库对应的类别中,有效地提高图像检索效率。

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