Saliency Detection via Dense and Sparse Reconstruction

By Xiaohui Li, Huchuan Lu, Lihe Zhang, Xiang Ruan, Ming-Hsuan Yang. IEEE International Conference on Computer Vision, 2013

708 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/2583797b4e5ee1a4bfc9da9d76e44ece4329170e

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In Sensory Systems

Citations in the corpus

Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images (2019, methodology): “[20] used the reconstruction error to measure the saliency of a region, where the salient region corresponds to a larger reconstruction error.”
Saliency detection by multi-context deep learning (2015, background): “A large number of approaches [63, 52, 40, 39, 32, 35, 60, 57, 56, 47, 41, 31, 27, 25, 24, 23, 11, 44, 17, 8, 13, 1, 21] are proposed to capture different saliency cues.”
Recurrent Attentional Networks for Saliency Detection (2016, methodology): “DRFI, RBD, DSR, MC, and HS are the top-performing methods evaluated in [4], while RRWR and BSCA are two very recent saliency detection works.”
Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring (2019, neutral): “Model [8] [30] [15] [7] [31] OURS Fβ .”
Salient Object Detection: A Discriminative Regional Feature Integration Approach (2013, methodology): “To save the space, we only consider the top four models ranked in the survey [23]: SVO [51], CA [17], CB [32], and RC [15] and recently-developed methods: SF [21], LRK [78], HS [33], GMR [48], PCA [31], MC [50], DSR [49], RBD [55] that are not covered in [23].”
Learning Uncertain Convolutional Features for Accurate Saliency Detection (2017, methodology): “We compare the proposed UCF algorithm with other 10 state-of-the-art ones including 6 deep learning based algorithms (DCL [24], DS [26], ELD [22], LEGS [44], MDF [50], RFCN [45]) and 4 conventional counterparts (BL [41], BSCA [33], DRFI [18], DSR [25]).”
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection (2017, methodology): “We compare our algorithm with other 11 state-of-the-art ones including 7 deep learning based algorithms (DCL [22], DHS [27], DS [24], ELD [20], LEGS [41], MDF [50], RFCN [44]) and 4 conventional algorithms (BL[39], BSCA [32], DRFI [19], DSR [23]).”
A Stagewise Refinement Model for Detecting Salient Objects in Images (2017, methodology): “In [30], dense and sparse reconstruction errors based on background prior are utilized for saliency detection.”
Minimum Barrier Salient Object Detection at 80 FPS (2015, methodology): “Salient regions can be inferred by their dissimilarity in appearance [19, 16, 23], or their connectivity [39, 33, 36] with respect to the image boundary regions.”
Salient Object Detection: A Benchmark (2015, neutral): “E .528 15 GMR [78] CVPR 2013 M .149 16 DRFI [79] CVPR 2013 C .697 17 PCA [80] CVPR 2013 M + C 4.34 18 LBI [81] CVPR 2013 M + C 251. 19 GC [82] ICCV 2013 C .037 20 CHM [83] ICCV 2013 M + C 15.4 21 DSR [84] ICCV 2013 M + C 10.2 22 MC [85] ICCV 2013 M + C .195 23 UFO [86] ICCV 2013 M + C 20.3 24 MNP [5”
Saliency detection via Cellular Automata (2015, methodology): “(b)-(f) Saliency maps generated respectively by HS [48], DSR [23], MR [49], wCO [52] and our algorithm BSCA.”
Salient object detection: A survey (2014, methodology): “In [129], saliency computation is formulated as the dense and sparse reconstruction errors w.”
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection (2015, methodology): “We qualitatively and quantitatively compare the proposed approach with several state-of-the-art methods including DRFI [33], Wco [26], IT [11], SF [50], GC [6], GMR [25], FT [2], MC [5], GB [1], HS [24], SVO [4], DSR [51], LEGS [34], BL15 [52] and BSCA [27].”
Review of Visual Saliency Detection With Comprehensive Information (2018, methodology): “In addition, some traditional techniques are also introduced to achieve image saliency detection, such as frequency domain analysis [50], sparse representation [22], cellular automata [25], random walks [26], low-rank recovery [29], and Bayesian theory [30].”
A Simple Pooling-Based Design for Real-Time Salient Object Detection (2019, background): “Recently, benefiting from the powerful feature extraction capability of CNNs, most of the traditional saliency detection methods based on hand-crafted features [3, 12, 20, 31] have been gradually surpassed.”
Progressive Attention Guided Recurrent Network for Salient Object Detection (2018, methodology): “Salient Object Detection methods can be categorized as conventional low-level hand-crafted features based [20, 4, 33, 15, 9, 38, 23, 21] and Convolutional Neural Networks driven [25, 13, 37, 14, 12, 16, 27, 18, 7, 35, 36, 28] approaches.”
Enhanced-alignment Measure for Binary Foreground Map Evaluation (2018, methodology): “…10 state-of-the-art (SOTA) salient object detection models including 3 traditional ones (ST [Liu et al. , 2014], DRFI [Wang et al. , 2017], and DSR [Li et al. , 2013]) and 7 deep learning based ones (DCL [Li and Yu, 2016], RFCN [Wang et al. , 2016], MC [Zhao et al. , 2015], MDF [Li and Yu, 2015],…”
Deep networks for saliency detection via local estimation and global search (2015, neutral): “Saliency cues such as center and object bias [31, 22], contrast information [38] and background prior [33, 15] have been shown to be effective in previous work.”