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arXiv 1304.1995cs.CVcs.DBcs.LG
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基于直方图分解和上下文相似性学习的图像检索

Image Retrieval using Histogram Factorization and Contextual Similarity Learning

Ocean University of China(中国海洋大学);Nanjing University(南京大学)

Liu Liang

摘要:

图像检索一直是计算机视觉和机器学习领域的热门课题。基于内容的图像检索,试图从数据库中检索视觉上与查询图像相似的图像,引起了人们的广泛关注。图像检索的两个最重要的问题是图像的表示和排序。最近,基于词袋的方法已经显示出其作为表示方法的能力。此外,非负矩阵分解也是一种流行的方法来表示数据样本。此外,上下文相似性学习也被研究和证明是一个有效的方法的排名问题。然而,这些技术从未被一起使用过。在本文中,我们开发了一个有效的图像检索系统表示每幅图像使用的词袋的方法直方图,然后应用非负矩阵分解,分解的直方图,最后学习排名分数使用上下文相似性学习方法。在一个大规模的图像数据库上对该系统进行了测试,结果表明了该系统的有效性。

英文摘要:

Image retrieval has been a top topic in the field of both computer vision and machine learning for a long time. Content based image retrieval, which tries to retrieve images from a database visually similar to a query image, has attracted much attention. Two most important issues of image retrieval are the representation and ranking of the images. Recently, bag-of-words based method has shown its power as a representation method. Moreover, nonnegative matrix factorization is also a popular way to represent the data samples. In addition, contextual similarity learning has also been studied and proven to be an effective method for the ranking problem. However, these technologies have never been used together. In this paper, we developed an effective image retrieval system by representing each image using the bag-of-words method as histograms, and then apply the nonnegative matrix factorization to factorize the histograms, and finally learn the ranking score using the contextual similarity learning method. The proposed novel system is evaluated on a large scale image database and the effectiveness is shown.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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