Intrinsic shape matching has become the standard approach for pose invariant correspondence estimation among deformable shapes. Most existing approaches assume global consistency. While global isometric matching is well understood, only a few heuristic solutions are known for partial matching. Partial matching is particularly important for robustness to topological noise, which is a common prob...

Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one correct. The key deal such problem disambiguate label sets and obtain correct assignments between instances their labels. In this paper, we interpret as instance-to-label matchings, reformulate task PLL matching selection problem. To model probl...

This work concerns the search for text compressors that compress better than existing dictionary coders, but run faster than statistical coders. We describe a new method for text compression using multiple dictionaries, one for each context of preceeding characters, where the contexts have varying lengths. The context to be used is determined using an escape mechanism similar to that of predict...

Feature-driven analysis forms the basis of many shape processing tasks, where detected feature points are characterized by local shape descriptors. Such descriptors have so far been defined to capture regions of interest centered at individual points. Using such regions to compare feature points can be problematic when performing partial shape matching, since the region of interest is typically...