RI: Small: Hierarchical Feature Learning by Heterogeneous Networks with Application to Face Verification

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

Learning good features is a key to computer vision problems such as recognizing human faces, and understanding scenes. Many computer vision researchers learn features by providing a semantic label for each image in a large database, limiting the amount of information per image to a few bits. Others learn features by identifying common patterns found in images such as lines, blobs, and more complicated shapes, but ignoring semantic information. This project develops algorithms to learn features that are common in images and also predict the semantics of images at various spatial scales using a new type of deep neural network called Heterogeneous Networks. The developed algorithms allow the incorporation of semantic information at intermediate layers. The algorithms developed can not only change how features are learned but also indicate how to scale feature learning to giant datasets of millions of images. The research team addresses challenging problems in human face verification using NCSA's petascale supercomputer, Blue Waters, and two large scale (millions of images) image data sets. The research of this projected is integrated with both undergraduate and graduate education. The results obtained from this project are applicable to a wide range of applications in computer vision and pattern recognition. The research team plans to release algorithms and face data sets collected in this project to research communities once they are finished.