Geodesic Saliency Using Background Priors

By Yichen Wei, Fang Wen, Wangjiang Zhu. European Conference on Computer Vision, 2012

884 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a074c93f8e74f8aff906bb1bea1c971fd737c4b3

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In Computer Vision and Pattern Recognition

Citations in the corpus

Shifting More Attention to Video Salient Object Detection (2019, background): “, center-surround contrast [12], background prior [79]) and cognitive theories of visual attention (e.”
How to Evaluate Foreground Maps (2014, background): “Many algorithms that output a non-binary map still compare against a binary groundtruth [1, 9, 10, 12, 13, 19, 21, 24].”
Saliency Detection via Dense and Sparse Reconstruction (2013, methodology): “Similar to [21], the objects with low consistency scores are removed.”
Contextual Hypergraph Modelling for Salient Object Detection (2013, methodology): “These approaches model uniqueness by exploiting the global statistical properties of the image, including frequency spectrum analysis [4], color-spatial distribution modeling [7], high-dimensional Gaussian filtering [11], low-rank matrix decomposition [12], and geodesic distance computation [5].”
Salient Object Detection: A Discriminative Regional Feature Integration Approach (2013, background): “Object prior, such as connectivity prior [45], concavity context [34], auto-context cue [48], and the background prior [53] are also studied for saliency computation.”
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection (2016, methodology): “Background prior [9-11] hypothesizes that regions near image boundaries are probably backgrounds.”
Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement (2015, methodology): “Recently, several methods [46], [51] exploited some information of the background, called boundary prior .”
Salient Object Detection via Structured Matrix Decomposition (2017, methodology): “427 Metric SMD ULR [26] SLR [28] LRR [27] GS [24] SF [50] CB [19] CA [7] SS [15] SEG [13] FT [49] SR [14] LC [16] WF↑ 0.”
CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion (2018, background): “[23] developed the boundary and connectivity priors about the background in images and calculated saliency in terms of the geodesic distance from the foreground to the image boundary.”
Non-local Deep Features for Salient Object Detection (2017, methodology): “Saliency maps produced by the GS [46], MR [49], wCtr* [52], BSCA [38], LEGS [43], MC [50], MDF [25] and DCL [26] methods compared to our NLDF method.”
Salient object detection: A survey (2014, methodology): “In [99], a undirected weighted graph G = (V, E) is built, where the vertices are the regions (or patches) plus the pseudo-background B, i.”
Hierarchical Saliency Detection (2013, methodology): “Local methods [13, 10, 1, 15] rely on pixel/region difference in the vicinity, while global methods [2, 4, 23, 30] rely mainly on color uniqueness in terms of global statistics.”
Oriented Object Proposals (2015, background): “The background prior has been shown to be effective in object-related tasks, such as salient object detection [29] [30].”
Saliency Detection via Graph-Based Manifold Ranking (2013, methodology): “We compare our method with fourteen state-of-the-art saliency detection algorithms: the IT [17], GB [14], MZ [25], SR [15], AC [1], Gof [11], FT [2], LC [37], RC [9], SVO [7], SF [27], CB [18], GS SP [34] and XIE [35] methods.”
Hierarchical Image Saliency Detection on Extended CSSD (2016, methodology): “Among them, image-based salient object detection [1], [2], [3], [4], [5] is an important stream, which can benefit several applications including detection [6], classification [7], retrieval [8], and object co-segmentation [9], for optimizing and saving computation.”
Stereo Video Object Segmentation Using Stereoscopic Foreground Trajectories (2019, methodology): “vision applications [18].”
Saliency Detection via Absorbing Markov Chain (2013, positive): “7 as the final ground truth as done in [33].”
Distilling Localization for Self-Supervised Representation Learning (2020, methodology): “For generating our augmentations, several saliency estimation methods are examined, including unsupervised non-learning based techniques [54, 45, 43], and a saliency network [32].”
Salient object detection via bootstrap learning (2015, methodology): “Left to right: (a) input (b) GS SP [34] (c) wCO [43] (d) LRMR [30] (e) GMR [38] (f) DSR [23] (g) XL13 [36] (h) HS [37] (i) RC-J [9] (j) GC [10] (k) SF [28] (l) Ours (m) wCO-bootstrapped (n) ground truth.”