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基于SIFT,K-Means和LDA的图像检索算法

汪宇雷 毕树生 孙明磊 蔡月日

汪宇雷, 毕树生, 孙明磊, 等 . 基于SIFT,K-Means和LDA的图像检索算法[J]. 北京航空航天大学学报, 2014, 40(9): 1317-1322. doi: 10.13700/j.bh.1001-5965.2013.0601
引用本文: 汪宇雷, 毕树生, 孙明磊, 等 . 基于SIFT,K-Means和LDA的图像检索算法[J]. 北京航空航天大学学报, 2014, 40(9): 1317-1322. doi: 10.13700/j.bh.1001-5965.2013.0601
Wang Yulei, Bi Shusheng, Sun Minglei, et al. Image retrieval algorithm based on SIFT, K-means and LDA[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(9): 1317-1322. doi: 10.13700/j.bh.1001-5965.2013.0601(in Chinese)
Citation: Wang Yulei, Bi Shusheng, Sun Minglei, et al. Image retrieval algorithm based on SIFT, K-means and LDA[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(9): 1317-1322. doi: 10.13700/j.bh.1001-5965.2013.0601(in Chinese)

基于SIFT,K-Means和LDA的图像检索算法

doi: 10.13700/j.bh.1001-5965.2013.0601
基金项目: 国家自然科学基金资助项目(51205011)
详细信息
    作者简介:

    汪宇雷(1989-),男,江西上饶人,硕士生,wyl37055122@163.com.

  • 中图分类号: TP391

Image retrieval algorithm based on SIFT, K-means and LDA

  • 摘要: 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,Scale Invariant Feature Transform),K-Means和潜在狄利克雷分布(LDA,Latent Dirichlet Allocation)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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出版历程
  • 收稿日期:  2013-10-22
  • 刊出日期:  2014-09-20

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