首页> 外文会议>Neural Information Processing pt.1; Lecture Notes in Computer Science; 4232 >Imbalanced Learning in Relevance Feedback with Biased Minimax Probability Machine for Image Retrieval Tasks
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Imbalanced Learning in Relevance Feedback with Biased Minimax Probability Machine for Image Retrieval Tasks

机译:有偏极小概率机用于图像检索任务的相关反馈中的不平衡学习

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In recent years, Minimax Probability Machine (MPM) have demonstrated excellent performance in a variety of pattern recognition problems. At the same time various machine learning methods have been used on relevance feedback tasks in Content-based Image Retrieval (CBIR). One of the problems in typical techniques for relevance feedback is that they treat the relevant feedback and irrelevant feedback equally. In other words, the negative instances largely outnumber the positive instances. Hence, the assumption that they are balanced is incorrect. In this paper we study how MPM can be applied to image retrieval, more precisely, Biased MPM during the relevance feedback iterations. We formulate the relevance feedback based on a modified MPM called Biased Minimax Probability Machine (BMPM). Different from previous methods, this model directly controls the accuracy of classification of the future data to build up biased classifiers. Hence, it provides a rigorous treatment on imbalanced data. Mathematical formulation and explanations are provided for showing the advantages. Experiments are conducted to evaluate the performance of our proposed framework, in which encouraging and promising experimental results are obtained.
机译:近年来,Minimax概率机(MPM)在各种模式识别问题中均表现出出色的性能。同时,基于内容的图像检索(CBIR)中的相关性反馈任务已使用了多种机器学习方法。相关反馈的典型技术中的问题之一是,它们平等地对待相关反馈和无关反馈。换句话说,否定实例大大超过了肯定实例。因此,假设它们是平衡的是不正确的。在本文中,我们研究了如何在相关性反馈迭代过程中将MPM应用于图像检索,更准确地说是有偏MPM。我们基于一种改进的MPM(称为“偏最小极大概率机”(BMPM))来制定相关性反馈。与以前的方法不同,此模型直接控制未来数据分类的准确性,以建立有偏的分类器。因此,它对不平衡的数据提供了严格的处理。提供数学公式和说明以显示优点。进行实验以评估我们提出的框架的性能,其中获得了令人鼓舞和有希望的实验结果。

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