Object localization is the task of locating objects in an image, typically by finding bounding boxes that isolate those objects. Identifying objects in images that have not had regions of interest labeled by humans often requires object localization to be performed first. The sliding window method is a common naïve approach, wherein the image is covered with bounding boxes of different sizes that form windows in the image. An object classifier is then run on each of these windows to determine if each given window contains a given object. However, because object classification algorithms tend to be computationally expensive, it is helpful to have an effective filter to reduce the number of times those classifiers have to be run.
In this thesis I evaluate one promising approach to object localization: the objectness algorithm proposed by Alexe et al. Specifically, I verify the results given by Alexe et al., and further explore the weaknesses and strengths of their "objectness"
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-3958 |
| Date | 18 May 2016 |
| Creators | Coates, Lewis Richard James |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
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