A Weighted Sparse Coding Framework for Saliency Detection

Nianyi Li, Bilin Sun, Jingyi Yu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5216-5223

Abstract


There is an emerging interest on using high-dimensional datasets beyond 2D images in saliency detection. Examples include 3D data based on stereo matching and Kinect sensors and more recently 4D light field data. However, these techniques adopt very different solution frameworks, in both type of features and procedures on using them. In this paper, we present a unified saliency detection framework for handling heterogenous types of input data. Our approach builds dictionaries using data-specific features. Specifically, we first select a group of potential background superpixels to build a primitive non-saliency dictionary. We then prune the outliers in the dictionary and test on the remaining superpixels to iteratively refine the dictionary. Comprehensive experiments show that our approach universally outperforms the state-of-the-art solution on all 2D, 3D and 4D data.

Related Material


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[bibtex]
@InProceedings{Li_2015_CVPR,
author = {Li, Nianyi and Sun, Bilin and Yu, Jingyi},
title = {A Weighted Sparse Coding Framework for Saliency Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}