自动自适应元数据生成图像检索

2005 Symposium on Applications and the Internet Workshops (SAINT 2005 Workshops) Pub Date : 2005-01-31 DOI:10.1109/SAINTW.2005.39

H. Sasaki, Y. Kiyoki

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引用次数: 3

摘要

本文提出了一种基于样本图像内容分析的自适应元数据自动生成系统。首先，我们的系统通过使用CBIR(计算样本图像和查询图像之间的结构相似性)来筛选不合适的查询图像以生成元数据。其次，系统通过选择在结构上与查询图像相似的样本图像附加的样本索引来生成元数据。第三，系统检测不正确的元数据，并通过识别错误选择的元数据重新生成正确的元数据。我们的系统将元数据生成的召回率提高了23.5%，沉降率提高了37%，而不仅仅是使用内容分析的结果。

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Automatic Adaptive Metadata Generation for Image Retrieval

In this paper, we present an automatic adaptive metadata generation system using content analysis of sample images. First, our system screens out improper query images for metadata generation by using CBIR that computes structural similarity between sample images and query images. Second, the system generates metadata by selecting sample indexes attached to the sample images that are structurally similar to query images. Third, the system detects improper metadata and re-generates proper metadata by identifying wrongly selected metadata. Our system has improved metadata generation by 23.5% on recall ratio and 37% on fallout ratio rather than just using the results of content analysis.

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2005 Symposium on Applications and the Internet Workshops (SAINT 2005 Workshops)

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