Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings

Understanding the visual content of images is one of the most important topics in computer vision. Many researchers have tried to teach the machine to see and perceive like human. In this dissertation, we develop several new approaches for image understanding with applications to affective computing, and person detection and recognition. Our proposed method applied to fashion photo analysis can understand the aesthetic quality of photos. Further, a bilinear model that takes into account the relative confidence of region proposals and the mutual relationship between multiple labels is developed to boost multi-label classification. It is evaluated both on object recognition and aesthetic attributes learning. We also develop a person detection and recognition system in natural settings that can robustly handle various pose, viewpoints, and lighting conditions. The system is then put into several real scenarios that has different amount of labelled data. Our algorithm that utilizes unlabelled data reduces the effort needed for data annotation while achieving similar results as with labelled data.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year

2016

Author dc:creator

• Chen, Ming

Contributors dc:contributor

• Jan P Allebach
• Mireille Boutin
• Mary L Comer
• Edward J Delp
• Qian Lin

• image understanding
• person detection
• person recognition
• photo aesthetics

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/1362

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2578