A Shape-Based Approach for Salient Object Detection Using Deep Learning
Abstract
Salient object detection is a key step in many image analysis tasks as it not only identifies relevant parts of a visual scene but may also reduce computational complexity by filtering out irrelevant segments of the scene. In this paper, we propose a novel salient object detection method that combines a shape prediction driven by a convolutional neural network with the mid and low-region preserving image information. Our model learns a shape of a salient object using a CNN model for a target region and estimates the full but coarse saliency map of the target image. The map is then refined using image specific low-to-mid level information. Experimental results show that the proposed method outperforms previous state-of-the-arts methods in salient object detection.
Cite
Text
Kim and Pavlovic. "A Shape-Based Approach for Salient Object Detection Using Deep Learning." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46493-0_28Markdown
[Kim and Pavlovic. "A Shape-Based Approach for Salient Object Detection Using Deep Learning." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/kim2016eccv-shape/) doi:10.1007/978-3-319-46493-0_28BibTeX
@inproceedings{kim2016eccv-shape,
title = {{A Shape-Based Approach for Salient Object Detection Using Deep Learning}},
author = {Kim, Jongpil and Pavlovic, Vladimir},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {455-470},
doi = {10.1007/978-3-319-46493-0_28},
url = {https://mlanthology.org/eccv/2016/kim2016eccv-shape/}
}