ACM SIGMM Award for Outstanding Technical Contributions to Multimedia Computing, Communications and Applications

 

Dr. Rui is a Fellow of ACM, IEEE, IAPR and SPIE. His contributions to multimedia research have persistently spanned the past two decades, via his 200+ scientific publications (23,000+ total citations, and an h-index of 70), 65 issued patents, and more than 10 widely used commercial products.  

 

While it is easy to search billions of images and videos on the web today, it was not the case 20 years ago. Dr. Rui’s most distinctive scientific contribution is that he fundamentally changed image retrieval by bridging the human perceptual similarity and image-feature similarity through relevance feedback (TCSVT’98, 2300 citations).  He developed the first image object model, the intra- and inter-normalization processes, and a dynamic weight-updating schema, to enable relevance feedback at the image representation level, feature level, and similarity metric level. In 2000, Dr. Rui further formulated a constrained minimization problem to simultaneously solve for both the optimal image query vector and the optimal transformed feature space. This approach remains the most comprehensive solution to date. His pioneering contributions have led to a new line of research taken up by hundreds of researchers around the world. In fact, over the past 20 years, all the major multimedia conferences, e.g., ACM Multimedia and IEEE ICME, have held special sessions on this topic.  A Google search on “image retrieval with relevance feedback” returns 32,000 results, many of which followed the directions set by Dr. Rui.  Pushing this line of research further, Dr. Rui‘s two recent papers on using search engine’s “click-through” as an implicit relevance feedback signal, won the 2015 IEEE Trans. on Multimedia Best Paper Award, and the 2016 IEEE Signal Processing Society Best Paper Award.