Rectifying Self Organizing Maps for Automatic Concept Learning from Web Images

We attack the problem of learning concepts automati-cally 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 Rec-tifying Self Organizing Maps (RSOM). Given an image col-lection returned for a concept query, RSOM provides clus-ters pruned from outliers. Each cluster is used to train a model representing a different characteristics of the con-cept. 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. 1