Saliency detection based on seed propagation in a multilayer graph
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- 作者：Insung Hwang ; Sang Hwa Lee ; Jae Sung Park ; Nam Ik Cho
- 关键词：Saliency ; Seed propagation ; Multilayer ; Contrast prior ; Boundary prior ; Semi ; supervised learning
- 刊名：Multimedia Tools and Applications
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：76
- 期：2
- 页码：2111-2129
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
- 出版者：Springer US
- ISSN：1573-7721
- 卷排序：76
文摘
This paper presents a saliency detection algorithm based on the seed propagation in graph representation of image. First, the image is divided into over-segmented regions and a graph of the connected regions (nodes) with color similarity (edge weight) is constructed. Second, saliency seeds and background seeds are extracted based on contrast and boundary prior respectively. The contrast prior is used to select the regions to be used as saliency seeds, because salient regions usually have high contrast. Meanwhile, the regions for the background seeds are extracted from image boundaries, based on the observation that salient objects are seldom located on the image boundaries. Finally, using a semi-supervised learning framework, both saliency and background seeds are propagated to the entire nodes and the saliency of each node is calculated from the propagated results. In addition, we introduce a multilayer graph to further improve the performance, which makes the saliency values within an object more uniform. Comparison with the state-of-the-art methods shows that our algorithm yields comparable or better results on several datasets in terms of precision-recall curve, ROC curve, F-measure and area under ROC curve, and also show visually plausible results.