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A unified framework for image retrieval using keyword and visual features

机译:使用关键字和视觉功能进行图像检索的统一框架

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In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.
机译:本文提出了一种基于关键词注释和视觉特征的统一图像检索框架。在此框架中,基于少量手动标记图像的视觉特征构建了一组统计模型,以表示语义概念,并用于将关键字传播到其他未标记图像。当通过相关性反馈获得更多由用户隐式标记的图像时,将定期更新这些模型。从这个意义上说,关键字模型起着积累和记忆从用户提供的相关性反馈中学到的知识的作用。此外,针对按关键词场景查询和针对图像实例场景查询,分别提出了两组有效和高效的相似度度量和相关性反馈方案。在这些方案中,关键字模型与视觉功能结合在一起。尤其是,引入了一种新的基于熵的主动学习策略,以提高针对关键字查询的相关性反馈的效率。此外,提出了一种新的算法来估计搜索概念的关键词特征,以图像为例进行查询。它比两个现有的相关性反馈算法更合适。实验结果证明了所提出框架的有效性。

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