Canonical image selection from the web

The vast majority of the features used in today's commercially deployed image search systems employ techniques that are largely indistinguishable from text-document search - the images returned in response to a query are based on the text of the web pages from which they are linked. Unfortunately, depending on the query type, the quality of this approach can be inconsistent. Several recent studies have demonstrated the effectiveness of using image features to refine search results. However, it is not clear whether (or how much) image-based approach can generalize to larger samples of web queries. Also, the previously used global features often only capture a small part of the image information, which in many cases does not correspond to the distinctive characteristics of the category. This paper explores the use of local features in the concrete task of finding the single canonical images for a collection of commonly searched-for products. Through large-scale user testing, the canonical images found by using only local image features significantly outperformed the top results from Yahoo, Microsoft and Google, highlighting the importance of having these image features as an integral part of future image search engines.