We attack the problem of learning concepts automatically from noisy web image
search results. Going beyond low level attributes, such as colour and texture,
we explore weakly-labelled datasets for the learning of higher level concepts,
such as scene categories. The idea is based on discovering common
characteristics shared among subsets of images by posing a method that is able
to organise the data while eliminating irrelevant instances. We propose a novel
clustering and outlier detection method, namely Rectifying Self Organizing Maps
(RSOM). Given an image collection returned for a concept query, RSOM provides
clusters pruned from outliers. Each cluster is used to train a model
representing a different characteristics of the concept. The proposed method
outperforms the state-of-the-art studies on the task of learning low-level
concepts, and it is competitive in learning higher level concepts as well. It
is capable to work at large scale with no supervision through exploiting the
available sources.

本文提出了一种从嘈杂的 Web 图像搜索结果中自动学习概念的方法，旨在通过发现子集间共同特征并消除无关实例的方法，基于弱标签数据集学习更高层次的概念，并提出了一种名为 Rectifying Self Organizing Maps (RSOM) 的聚类和离群点检测方法。该方法在学习低级别概念的任务上优于现有研究，而且在学习高级别概念方面也极具竞争力，并且能够利用现有资源在大规模上无监督地工作。