Saliency Detection with Multi-Scale Superpixels

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Na Tong, Huchuan Lu, Lihe Zhang, Xiang Ruan
Journal/Conference Name IEEE Signal Processing Letters
Paper Category
Paper Abstract We propose a salient object detection algorithm via multi-scale analysis on superpixels. First, multi-scale segmentations of an input image are computed and represented by superpixels. In contrast to prior work, we utilize various Gaussian smoothing parameters to generate coarse or fine results, thereby facilitating the analysis of salient regions. At each scale, three essential cues from local contrast, integrity and center bias are considered within the Bayesian framework. Next, we compute saliency maps by weighted summation and normalization. The final saliency map is optimized by a guided filter which further improves the detection results. Extensive experiments on two large benchmark datasets demonstrate the proposed algorithm performs favorably against state-of-the-art methods. The proposed method achieves the highest precision value of 97.39% when evaluated on one of the most popular datasets, the ASD dataset.
Date of publication 2014
Code Programming Language MATLAB

Copyright Researcher 2022