Despite recent advances in the joint processing of images,
sometimes it may not be as effective as single image
processing for object discovery problems. In this paper, while
aiming for common object detection, we attempt to address
this problem by proposing a novel QCCE: Quality Constrained
Co-saliency Estimation method. The approach here is to iteratively
update the saliency maps through co-saliency estimation
depending upon quality scores, which indicate the degree of
separation of foreground and background likelihoods (the easier
the separation, the higher the quality of saliency map). In this
way, joint processing a by the quality
of saliency maps. Moreover, the proposed method can be applied
to both unsupervised and supervised scenarios, unlike other
methods which are particularly designed for one scenario only.
Experimental results demonstrate the superior performance of the
proposed method compared to the state-of-the-art methods.
Koteswar Rao Jerripothula (2021). Matlab code QCCE: Quality Constrained Co-saliency Estimation (https://github.com/jkoteswarrao/QCCE-Quality-Constrained-Co-saliency-Estimation), GitHub. Retrieved .
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