Title: Topics in Computational Visual Recognition
In this tutorial we will look at the technology behind the current state of the art methods for various high-level visual recognition tasks such as object detection, image classification and scene recognition. We will cover the fundamental concepts and also highlight some of the recent advances in the area and research directions. These include:
- Image representation - low level features and recent advances
- Learning - discriminative and generative models
- Object detection techniques - rigid template and part-based models
- Scene understanding - semantic spaces, topics in information retrieval and cross-modal search
Tutorial slides can be downloaded from here.
SpeakersSubhransu Maji Toyota Technological Institute at Chicago, USA
Subhransu Maji received the BTech degree in computer science and engineering from the Indian Institute of Technology Kanpur, in 2006, and the PhD degree in computer science from the University of California, Berkeley, in 2011. He is currently a research assistant professor at TTI Chicago. Earlier he was an intern in Google’s Image Search Group and INRIA’s LEAR Group, and a visiting researcher at Microsoft Research India and The Johns Hopkins University. His primary interests are in computer vision, with focus on representations and efficient algorithms for visual recognition. He received the medal for the best graduating student in computer science from IIT Kanpur. He was one of the recipients of the Google Graduate Fellowship in 2008.
Nikhil Rasiwasia received the B.Tech degree in electrical engineering from Indian Institute of Technology Kanpur in 2005. He received the MS and PhD degrees from the University of California, San Diego in 2007 and 2011 respectively, where he was a graduate student researcher at the Statistical Visual Computing Laboratory, in the ECE department. Currently, he is working as scientist for Yahoo Labs! Bangalore, India. In 2008, he was recognized as an `Emerging Leader in Multimedia' by IBM T. J. Watson Research. He also received the best student paper award at ACM Multimedia conference in 2010. His research interests are in the areas of computer vision and machine learning, in particular applying machine learning solutions to computer vision problems.