User-Powered "Content-Free" Approach to Image Retrieval

Consider a stereotypical image-retrieval problem; a user submits a set of query images to a system and through repeated interactions during which the system presents its current choices and the user gives his/her preferences to them, the choices are narrowed to the image(s) that satisfies the user. The problem obviously must deal with image content, i.e., interpretation and preference. For this purpose, conventional so-called contentbased image retrieval (CBIR) approach uses image-processing and computer-vision techniques, and tries to understand the image content. Such attempts have produced good but limited success, mainly because image interpretation is a highly complicated perceptive process. We propose a new approach to this problem from a totally different angle. It attempts to exploit the human’s perceptual capabilities and certain common, if not identical, tendencies that must exist among people’s interpretation and preference of images. Instead of processing images, the system simply accumulates records of user feedback and recycles them in the form of collaborative filtering, just like a purchase recommendation system such as Amazo.com. To emphasize the point that it does not deal with image pixel information, we dub the approach by a term “content-free” image retrieval (CFIR). We discuss various issues of image retrieval, argue for the idea of CFIR, and present results of preliminary experiment. The results indicate that the performance of CFIR improves with the number of accumulated feedbacks, outperforming a basic but typical conventional CBIR system.