||We present the MDS feature learning framework, in which multidimensional scaling (MDS) is applied on high-level pairwise image distances to learn fixed-length vector representa- tions of images. The aspects of the images that are captured by the learned features, which we call MDS features, completely depend on what kind of image distance measurement is employed. With properly selected semantics-sensitive image distances, the MDS features provide rich semantic information about the images that is not captured by other feature extraction techniques. In our work, we introduce the iterated Levenberg-Marquardt algorithm for solving MDS, and study the MDS feature learning with IMage Euclidean Distance (IMED) and Spatial Pyramid Matching (SPM) distance. We present experiments on both synthetic data and real images, the publicly accessible UIUC car image dataset. The MDS features based on SPM distance achieve exceptional performance for the car recognition task.
Short Biography: Professor Kim L. Boyer is the Head of the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute, USA. He received the Ph.D. degree in electrical engineering, from Purdue University in 1986. He is the current President of the International Association for Pattern Recognition. Dr. Boyer's research interests include all aspects of computer vision and medical image analysis. His work has ranged over aerial and satellite image understanding, robotics, cardiac magnetic resonance imaging, ophthalmic optical coherence tomography, interferometric image analysis, and more. Dr. Boyer has published six books and more than 100 scientific papers and has lectured in nearly 30 countries around the world.