Image Saliency Detection Based on Object Compactness and Regional Homogeneity Strategy
-
摘要: 针对基于图模型的显著性检测算法中节点间特征差异描述不准确的问题，该文提出一种目标紧密性与区域同质性策略相结合的图像显著性检测算法。区别于常用的图模型，该算法建立更贴近人眼视觉系统的稀疏图结构与新颖的区域同质性图结构，以便描述图像前景内部的关联性与前景背景间的差异性，从而摒弃众多节点的冗余连接，强化节点局部空间关系；并且结合聚类簇紧密性采取流形排序的方式形成显著图，利用背景区域簇的相似性，引入背景置信度进行显著性优化，最终得到精细的检测结果。在4个基准数据集上与4种基于图模型的流行算法对比，该算法能清晰地突出显著区域，且在多种综合指标评估中，具备更优越的性能。Abstract: Considering the inaccurate description of feature differences between nodes in the graph-based saliency detection algorithm, an image saliency detection algorithm combining object compactness and regional homogeneity strategy is proposed. Different from the commonly used graph-based model, a sparse graph-based structure closer to the human visual system and a novel regional homogeneity graph-based structure are established. They are used to describe the correlation within the foreground and the difference between foreground and background. Therefore, many redundant connections of nodes are eliminated and the local spatial relationship of nodes is strengthened. Then the clusters are combined to form a saliency map by means of manifold ranking. Finally, the background confidence is introduced for saliency optimization by the similarity of the background region clusters and the final detection result is obtained. Compared with 4 popular graph-based algorithms on the four benchmark datasets, the proposed algorithm can highlight the salient regions clearly and has better performance in the evaluation of multiple comprehensive indicators.
-
Key words:
- Graph-based model /
- Object compactness /
- Regional homogeneity /
- Manifold ranking /
- Saliency detection
-
CONG Runmin, LEI Jianjun, FU Huazhu, et al. Co-saliency detection for RGBD images based on multi-constraint feature matching and cross label propagation[J]. IEEE Transactions on Image Processing, 2018, 27(2): 568–579. doi: 10.1109/TIP.2017.2763819 WANG Songtao, ZHEN Zhou, WEI Jin, et al. Visual saliency detection for RGB-D images under a Bayesian framework[J]. IPSJ Transactions on Computer Vision and Applications, 2018, 10: 1. doi: 10.1186/S41074-017-0037-0 LIU Nian and HAN Junwei. A deep spatial contextual long-term recurrent convolutional network for saliency detection[J]. IEEE Transactions on Image Processing, 2018, 27(7): 3264–3274. doi: 10.1109/TIP.2018.2817047 WU Xiyin, JIN Zhong, ZHOU Jingbo, et al. Saliency propagation with perceptual cues and background-excluded seeds[J]. Journal of Visual Communication and Image Representation, 2018, 54: 51–62. doi: 10.1016/J.JVCIR.2018.04.006 LI Guanbin and YU Yizhou. Contrast-oriented deep neural networks for salient object detection[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(12): 6038–6051. doi: 10.1109/TNNLS.2018.2817540 TONG Na, LU Huchuan, ZHANG Lihe, et al. Saliency detection with multi-scale superpixels[J]. IEEE Signal Processing Letters, 2014, 21(9): 1035–1039. doi: 10.1109/LSP.2014.2323407 余春艳, 徐小丹, 钟诗俊. 面向显著性目标检测的SSD改进模型[J]. 电子与信息学报, 2018, 40(11): 2554–2561.YU Chunyan, XU Xiaodan, and ZHONG Shijun. An improved SSD model for saliency object detection[J]. Journal of Electronics &Information Technology, 2018, 40(11): 2554–2561. YANG Chuan, ZHANG Lihe, LU Huchuan, et al. Saliency detection via graph-based manifold ranking[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3166–3173. doi: 10.1109/CVPR.2013.407. WEI Yichen, WEN Fang, ZHU Wangjiang, et al. Geodesic saliency using background priors[C]. The 12th European Conference on Computer Vision, Florence, Italy, 2012: 29–42. doi: 10.1007/978-3-642-33712-3_3. ZHANG Lihe, YANG Chuan, LU Huchuan, et al. Ranking saliency[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(9): 1892–1904. doi: 10.1109/TPAMI.2016.2609426 LI Hongyang, LU Huchuan, LIN Zhe, et al. Inner and inter label propagation: salient object detection in the wild[J]. IEEE Transactions on Image Processing, 2015, 24(10): 3176–3186. doi: 10.1109/TIP.2015.2440174 YUAN Yuchen, LI Changyang, KIM J, et al. Reversion correction and regularized random walk ranking for saliency detection[J]. IEEE Transactions on Image Processing, 2018, 27(3): 1311–1322. doi: 10.1109/TIP.2017.2762422 ZHOU Li, YANG Zhaohui, ZHOU Zongtan, et al. Salient region detection using diffusion process on a two-layer sparse graph[J]. IEEE Transactions on Image Processing, 2017, 26(12): 5882–5894. doi: 10.1109/TIP.2017.2738839 ZHANG Zizhao, XING Fuyong, WANG Hanzi, et al. Revisiting graph construction for fast image segmentation[J]. Pattern Recognition, 2018, 78: 344–357. doi: 10.1016/J.PATCOG.2018.01.037 ZHANG Jinxia, FANG Shixiong, EHINGER K A, et al. Hypergraph optimization for salient region detection based on foreground and background queries[J]. IEEE Access, 2018, 6: 26729–26741. doi: 10.1109/ACCESS.2018.2834545 -
下载:
图(10) / 表(1)
计量
- 文章访问数: 2387
- HTML全文浏览量: 1426
- PDF下载量: 78
- 被引次数: 0
下载:
