Quasi-facetted retrieval of images using emotions?

As part of my literature catch up I found an extremely interesting paper in JASIST by S. Schmidt and Wolfgang G. Stock entitled, 'Collective indexing of emotions in images : a study in emotional information retrieval'. The motivation behind the research is simple: images tend to elicit emotional responses in people. Is it therefore possible to capture these emotional responses and use them in image retrieval?

An interesting research question indeed, and Schmidt and Stock's study found that 'yes', it is possible to capture these emotional responses and use them. In brief, their research asked circa 800 users to tag a variety of public images from Flickr using their scroll-bar tagging system. This scroll-bar tagging system allowed users to tag images according to a series of specially selected emotional responses and to indicate the intensity of these emotions. Schmidt and Stock found that users tended to have favourite emotions and this can obviously differ between users; however, for a large proportion of images the consistency of emotion tagging is very high (i.e. a large proportion of users frequently experience the same emotional response to an image). It's a complex area of study and their paper is recommended reading precisely for this reason (capturing emotions anyone?!), but their conclusions suggest that:

"…it seems possible to apply collective image emotion tagging to image information systems and to present a new search option for basic emotions."

To what extent does the image above (by D Sharon Pruitt) make you feel happiness, anger, sadness, disgust or fear? It is early days, but the future application of such tools could find a place within the growing suite of image filters that many search engines have recently unveiled. For example, yesterday Keith Trickey was commenting on the fact that the image filters in Bing are better than Google or Yahoo!. True. There are more filters, and they seem to work better. In fact, they provide a species of quasi-taxonomical facets: (by) size, layout, color, style and people. It's hardly Ranganathan's PMEST, but – keeping in mind that no human intervention is required - it's a useful quasi-facet way of retrieving or filtering images, albeit flat.

An emotional facet, based on Schmidt and Stock's research, could easily be added to systems like Bing. In the medium term it is Yahoo! that will be more in a position to harness the potential of emotional tagging. They own Flickr and have recently incorporated the searching and filtering of Flickr images within Yahoo! Image Search. As Yahoo! are keen for us to use Image Search to find CC images for PowerPoint presentations, or to illustrate a blog, being able to filter by emotions would be a useful addition to the filtering arsenal.