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Multi-label multi-instance learning with missing object tags

机译:缺少对象标签的多标签多实例学习

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摘要

In this paper, a novel framework is developed for leveraging large-scale loosely tagged images for object classifier training by addressing three key issues jointly: (a) spam tags e.g., some tags are more related to popular query terms rather than the image semantics; (b) loose object tags, e.g., multiple object tags are loosely given at the image level without identifying the object locations in the images; (c) missing object tags, e.g., some object tags are missed and thus negative bags may contain positive instances. To address these three issues jointly, our framework consists of the following key components for leveraging large-scale loosely tagged images for object classifier training: (1) distributed image clustering and inter-cluster visual correlation analysis for handling the issue of spam tags by filtering out large amounts of junk images automatically, (2) multiple instance learning with missing tag prediction for dealing with the issues of loose object tags and missing object tags jointly; (3) structural learning for leveraging the inter-object visual correlations to train large numbers of inter-related object classifiers jointly. Our experiments on large-scale loosely tagged images have provided very positive results.
机译:本文提出了一种新颖的框架,可通过共同解决三个关键问题来利用大规模松散标记的图像进行对象分类器训练:(a)垃圾邮件标签,例如,某些标签与流行的查询词而不是图像语义更相关; (b)松散的对象标签,例如,在图像级别松散地给出多个对象标签,而没有标识图像中的对象位置; (c)缺少对象标签,例如,某些对象标签被遗漏,因此负袋可能包含正例。为了共同解决这三个问题,我们的框架由以下关键组件组成,这些关键组件用于利用大规模松散标记的图像进行对象分类器训练:(1)分布式图像聚类和集群间视觉相关性分析,用于通过过滤处理垃圾邮件标签问题自动剔除大量垃圾图像;(2)多实例学习和标签缺失预测,共同处理对象标签松散和对象标签缺失的问题; (3)利用对象间视觉相关性共同训练大量相互关联的对象分类器的结构学习。我们对大规模的松散标记图像进行的实验提供了非常积极的结果。

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