Benchmark evaluation of object segmentation proposal
1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Computer Science and Engineering, Computer Science and Engineering
|Online Access:||PDF Full Text (PDF, )|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-201508291934
|Publish Date:|| 2015-08-31
|Thesis type:||Master's thesis (tech)
In this research, we provide an in depth analysis and evaluation of four recent segmentation proposals algorithms on PASCAL VOC benchmark. The principal goal of this study is to investigate these object detection proposal methods in an un-biased evaluation framework. Despite having a widespread application, the strengths and weaknesses of different segmentation proposal methods with respect to each other are mostly not completely clear in the previous works. This thesis provides additional insights to the segmentation proposal methods. In order to evaluate the quality of proposals we plot the recall as a function of average number of regions per image. PASCAL VOC 2012 Object categories, where the methodologies show high performance and instances where these algorithms suffer low recall is also discussed in this work. Experimental evaluation reveals that, despite being different in the operational nature, generally all segmentation proposal methods share similar strengths and weaknesses. The analysis also show how one could select a proposal generation method based on object attributes. Finally we show that, improvement in recall can be obtained by merging the proposals of different algorithms together. Experimental evaluation shows that this merging approach outperforms individual algorithms both in terms of precision and recall.
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