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Feature Quality-Based Dynamic Feature Selection for Improving Salient Object Detection
Abstract
Salient object detection is typically accomplished by combining the outputs of multiple primitive feature detectors (that output feature maps or features). The diversity of images means that different basic features are useful in different contexts, which motivates the use of complementary feature detectors in a general setting. However, naive inclusion of features that are not useful for a particular image leads to a reduction in performance. In this paper, we introduce four novel measures of feature quality and then use those measures to dynamically select useful features for the combination process. The resulting saliency is thereby individually tailored to each image. Using benchmark data sets, we demonstrate the efficacy of our dynamic feature selection system by measuring the performance enhancement over the state-of-the-art models for complementary feature selection and saliency aggregation tasks. We show that a salient object detection technique using our approach outperforms competitive models on the PASCAL VOC 2012 dataset. We find that the most pronounced performance improvements occur in challenging images with cluttered backgrounds, or containing multiple salient objects.
Feature Quality-Based Dynamic Feature Selection for Improving Salient Object DetectionpdfProjects at Bangalore offers Final Year students Engineering projects - ME projects,M.Tech projects,BE Projects,B.Tech Projects, Diploma Projects,Electronics Projects,ECE Projects,EEE Projects,Mechanical projects,Bio-Medical Projects,Telecommunication Projects,Instrumentation Projects,Software Projects - MCA Projects,M.Sc Projects,BCA Projects,B.Sc Projects,Science Exhibition Kits,Seminars,Presentations,Reports and Power System Projects,Arduino Projects,IEEE ECE Projects,Raspberry pi Projects,VHDL Projects,SIMULINK Projects,MATLAB Projects,Mtech internship etc
