使用Iconclass改进视觉词包

Naoki Motohashi, K. Yamauchi, T. Takagi
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引用次数: 1

摘要

近年来,视觉词袋作为一种利用图像定义特征的图像检索方法受到了广泛的关注。然而,通常用于视觉词袋的k-means聚类有一个缺点,即其结果会受到初始点及其数量的设置的影响。此外,关键点增加越多,处理成本就越高。我们利用自己开发的一种量化方法解决了视觉词袋问题。此外,我们还开发了一个使用本体的主题理解系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improvement of bag of visual words using Iconclass
Recently, bag-of-visual-words has been paid attention to as an image retrieval approach that uses the defining features of images. However, k-means clustering generally used in bag-of-visual-words has a drawback such that its result is affected by setting up initial points and their number. Additionally, the more keypoints increase, the more expensive processing becomes. We resolve the problem of bag-of-visual-words by using a quantizing method that we have developed. In addition, we have developed a theme comprehending system that uses ontology.
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