Performance Evaluation of Object Proposal Generators for Salient Object Detection
This thesis proposes a novel evaluation framework that analyses the performance of popular existing object proposal generators in detecting the most salient objects. This work also shows that, by incorporating saliency constraints, the number of generated object proposals and thus the computational cost can be decreased significantly for a target true positive detection rate (TPR).
As part of the proposed framework, salient ground-truth masks are generated from the given original ground-truth masks for a given dataset. Given an object detection dataset, this work constructs salient object location ground-truth data, referred to here as salient ground-truth data for short, that only denotes the locations of salient objects. This is obtained by first computing a saliency map for the input image and then using it to assign a saliency score to each object in the image. Objects whose saliency scores are sufficiently high are referred to as salient objects. The detection rates are analyzed for existing object proposal generators with respect to the original ground-truth masks and the generated salient ground-truth masks.
As part of this work, a salient object detection database with salient ground-truth masks was constructed from the PASCAL VOC 2007 dataset. Not only does this dataset aid in analyzing the performance of existing object detectors for salient object detection, but it also helps in the development of new object detection methods and evaluating their performance in terms of successful detection of salient objects.]]>autKotamraju, Sai PrajwalthsKaram, Lina JdgcYu, HongbindgcJayasuriya, SurenpblArizona State UniversityengMasters Thesis Electrical Engineering 2019https://hdl.handle.net/2286/R.I.5348900Masters ThesisAcademic theses70 pages115579410811630032421157065systemIn Copyright2019TextElectrical EngineeringComputer ScienceImage Saliencyobject detectionObject Proposal GenerationSalient Object Detection