[Defense] Defining and Assessing a Novel Spatio-Chromatic Basis for Unsupervised, Class-Agnostic Scene Segmentation
Thursday, July 22, 2021
10:00 am - 11:30 am
will defend her thesis
Defining and Assessing a Novel Spatio-Chromatic Basis for Unsupervised, Class-Agnostic Scene Segmentation
This thesis defines a novel image feature, the “semantic pattern region”, measures the strength of its correlation to scene semantics, and demonstrates its use for unsupervised, class-agnostic scene segmentation. The semantic pattern region is derived from spatio-chromatic image partitions and has an inherent one-to-one relationship with the semantic regions of the original image.
I will demonstrate the pattern region’s potential utility for segmentation in two ways: 1) by presenting representative examples of the best and worst results produced by the segmentation algorithm, and 2) by reporting the results of a quantitative assessment confirming a strong one-to-one spatio-chromatic correspondence between pattern regions and semantic and salient ground truth regions.
10:00AM - 11:30AM CT
Online via TBD
Dr. Shishir Shah, thesis advisor
Faculty, students and the general public are invited.