For most large-scale image retrieval systems, performance depends upon accurate meta-data. While content-based image retrieval has progressed in recent years, typically image contributors must provide appropriate keywords or tags that describe the image. Tagging, however, is a difficult and time-consuming task, especially for non-native English speaking contributors.

At Shutterstock, we mitigate this problem for our contributors by providing automatic tag recommendations. In this talk, delivered as a Webinar for Bright Talk’s “Business Intelligence and Analytics” channel, I describe the machine learning system behind the keyword recommendation system which Shutterstock’s Search and Algorithm Teams developed and deployed to the site.

Tag co-occurrence forms the basis of the recommendation algorithm. Co-occurrence is also the basis for some previous systems of tag recommendation deployed in the context of popular photo sharing services such as Flickr. In the context of online stock photography, tag recommendation has several aspects which are different from the context of photo sharing sites. In online stock photography, contributors are highly motivated to provide high quality tags because they make images easier to find and consequently earn higher contributor revenue. In building the system, we explored several different recommendation strategies and found that significant improvements are possible as compared to a recommender that only uses tag co-occurrence.

The three principle points of the talk are as follows:

  1. we characterize tagging behavior in the stock photography setting and show it is demonstrably different from popular photo sharing services.
  2. we explore different tag co-occurrence measures and in contrast to previous studies and a linear combination of two different measures to be optimal, and
  3. we show that a novel strategy that incorporates similar images can expand contextual information and significantly improve the precision of recommended tags.