In this thesis, we address the action retrieval and the object category segmentation problems by directly optimizing application specific performance measures using deep learning. Most deep learning methods are designed to optimize simple loss functions (e.g., cross-entropy or hamming loss). These loss functions are suitable for applications where the performance of the application is measured by overall accuracy. But for many applications, the overall accuracy is not an appropriate performance measure. For example, applications like action retrieval often use the area under the Receiver Operating Characteristic curve (ROC curve) to measure the performance of a retrieval algorithm. Likewise, in object category segmentation from images, the intersection-over-union (IoU) is the standard performance measure. In this thesis, we propose approaches to directly optimize these complex performance measures in deep learning framework. / October 2016
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/31812 |
| Date | January 2016 |
| Creators | Rahman, Md Atiqur |
| Contributors | Wang, Yang (Computer Science), Leung, Carson (Computer Science) Hu, Pingzhao (Biochemistry and Medical Genetics) |
| Publisher | IEEE |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
Page generated in 0.0022 seconds