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Weighting scheme for image retrieval based on bag-of-visual-words

机译：基于视觉词袋的图像检索加权方案

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摘要

Inspired by the success of bag-of-words in text retrieval, bag-of-visual-words and its variants are widely used in content-based image retrieval to describe visual content. Various weighting schemes have also been proposed to integrate different yet complementary visual-words. However, most of these weighting schemes tend to use fixed weight for every visual-word extracted from the query image, which may lose the discriminative information. This study presents a novel combining method which captures query-specific weights for visual-words in query image. The method mainly contains two stages. Firstly, in offline weight learning, the authors introduce a linear classifier to build a query-category mapping table, and max-margin learning to build category-weight mapping table. Query-category mapping table is used to map the query image to the most likely image class, and category-weight mapping table is used to map image class to the weights of visual-words. Secondly, in online weight mapping, the weights of visual-words are determined efficiently by looking into the pre-learned mapping tables. Experimental results on WANG database and Caltech 101 demonstrate that the proposed weighting scheme can effectively weight visual-words of query image according to their discriminative information. In addition, comparative experiments demonstrate the proposed weighting scheme can obtain higher retrieval performance than other weighting schemes.
机译：受到词袋在文本检索中成功的启发，视觉词袋及其变体被广泛用于基于内容的图像检索中，以描述视觉内容。还提出了各种加权方案以集成不同但互补的视觉词。但是，大多数这些加权方案都倾向于对从查询图像中提取的每个视觉词使用固定的权重，这可能会丢失判别信息。这项研究提出了一种新颖的组合方法，该方法可以捕获查询图像中视觉单词的查询特定权重。该方法主要包括两个阶段。首先，在离线权重学习中，作者引入了线性分类器来构建查询-类别映射表，并在最大余量学习中构建了类别-权重映射表。查询类别映射表用于将查询图像映射到最可能的图像类别，类别权重映射表用于将图像类别映射到视觉单词的权重。其次，在在线权重映射中，通过查看预先学习的映射表可以有效地确定视觉单词的权重。在WANG数据库和Caltech 101上的实验结果表明，所提出的加权方案可以根据查询图像的识别信息有效地对查询词进行加权。另外，对比实验表明，该加权方案比其他加权方案具有更高的检索性能。

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