Exploiting Multiple Web Resources towards Collecting Positive Training Samples for Visual Concept Learning

The number of images uploaded to the web is enormous and is rapidly increasing. The, purpose of our work is to use these for acquiring positive training data for visual concept learning. Manually creating training data for visual concept classifiers is an expensive and time, consuming task. We propose an approach which automatically collects positive training samples from the Web by constructing a multitude of text queries and retaining for each query only very few top-ranked images returned by each one of the different web image search engines (Google, Flickr and Bing). In this way, we sift the burden of false positive reject ion to the Web search engines and directly assemble a rich set of high quality positive training samples. Experiments on forty concepts, evaluated on the ImageNet dataset, show the merit of the proposed approach.