--------------------------------------------------------------------------------------------- *TALK I :* Title : Graph and Tree Kernels ( Learning with Kernel Project ) Speaker : Arnab Ghosh and Viveka Kulharia Timing : 4:00 PM - 4:30 PM , 16 January 16 Day : Saturday Abstract * : *In the real world similarity between graphs is an important measure needed for several real life problems like classification of Proteins and Enzymes and even in the Social Network analysis whereby 2 social graphs might have to be clustered by similar properties. In the quest to determine the similarity between pairs of graphs in 2005 Borgwardt et al looked at the similarity between the graphs in terms of shortest path kernels which was an improvement over the random walk kernels over product graphs which had its limitations on tractability but in 2010 SVN Vishwanathan et al came up with a novel method to compute random walk kernels up to any length and that became much faster in terms of computation than the Shortest Path Kernel .We looked at the 2010 paper and implemented the methods for computing the random walk kernel that were used in the paper and also implemented the shortest path kernel as shown by the 2005 paper and compared the running times and the kernels obtained from them. ---------------------------------------------- *TALK II:* Title : Vectorization of Real World Sketches Speaker : Akshay Masare Timing : 4:30 PM - 5:00 PM , 16 January 16 Day : Saturday Abstract * : *There are still many artists who prefer to first sketch out their ideas with pen and paper before recreating them digitally. During the pre-design phase as well, pen and paper is the most used way of sketching. But, the problem with using pen and paper in product design is that it captures only one view of the object, and viewing from multiple views is not possible. There have been attempts of using multiple sketch views for generating a 3- dimensional view of the object, but the difficulty that all these methods faced was detecting the borders of the object. Usually edge detectors are used to find complex shapes like junctions, straight lines and curves. But, sketch tokens provide a novel approach to learning these features and detecting edge-based mid-level features. The approach given was to learn supervised mid-level information from human labelled edges. As, there is no good dataset available for hatched images, we create one. The hatched lines in the images are usually drawn by the artists in response to the contour of the object surface and the way light hits the surface. As extracting these characteristics from a 2 dimensional image is not possible, we decided to generate hatch lines on 3 dimensional models --------------------------------------------------------------------------