Reading #12. Constellation Models for Sketch Recognition

Comments on Others:

Marty

Summary

            This paper describes a constellation or pictorial structure model to recognize strokes in sketches by capturing the structure of a particular class of objects and based on the local features of it. Learning of the system is done by probabilistic model from example sketches with known stroke labeling. Then the recognition algorithm determines a maximum likelihood labeling for an unlabelled sketch by searching through the space of possible label assignments using multi pass branch and bound algorithm. In the model, object representation is done by a constellation model, which based on the features of pairs of parts. Due to the complexity of n number of features, each of these features is broken into mandatory and optional.

            According to the authors, the sketch recognition process has two phases, first to search possible mandatory labels and then optional labels. The searching is based on ML search procedure and search over possible label assignments are done by branch-bound search tree.  Authors proposed a multiple threshold and hard constraints to avoid situations with higher mandatory labels or higher number of strokes.

Discussion

             The idea of using a constellation model for the features of a sketch is an interesting one. I’m just wondering how useful this for our second project to identify course of action diagrams????

            In my opinion restricting a user to draw a sketch based on the assumption that mandatory and optional features existence is sort of a constraining users freehand sketching. Yes, this makes the life much easier for the authors by making the searching is less complex, but I guess they are forgetting the golden rule of sketching recognition……….”freehand sketching”……….. 

Find the paper here.