Saliency detection via Cellular Automata

By Yao Qin, Huchuan Lu, Yiqun Xu, He Wang. 2015

493 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sensory Systems

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Citations in the corpus

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017, methodology): “The proposed dual-stage attention-based recurrent neural network (DA-RNN) not only can be used for time series prediction, but also has the potential to serve as a general feature learning tool in computer vision tasks [Pu et al. , 2016; Qin et al. , 2015].”
Deep Contrast Learning for Salient Object Detection (2016, methodology): “We compare our saliency models (DCL and DCL+) against eight recent state-of-the-art methods, including SF [38], GC [7], DRFI [20], PISA [42], BSCA [39], LEGS [43], MC [49] and MDF [25].”
Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking (2020, methodology): “Some methods simulate saliency detection in Bayesian inference models [10], [11] and sparse reconstruction [12], [13].”
A Stagewise Refinement Model for Detecting Salient Objects in Images (2017, background): “In [16, 15, 53, 23, 27, 38], saliency is measured by label propagation where initial labeling is propagated from the labeled elements to the unlabeled ones based on their pairwise affinities.”
Salient object detection: A survey (2014, background): “h image patch is proportional to its sampling probability from all of the patches, which are extracted from both the input image and the similar images retrieved from a corpus of unlabeled images. In [166], cellular automata is exploited for unsupervised salient object detection. Aggregation and opt”
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.”
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]).”
RGBD Salient Object Detection via Deep Fusion (2016, methodology): “2) Performance Comparison: In this section, we compare our method with four state-of-the-art methods designed for RGB images (S-CNN [41], BSCA [16], MB+ [53], and LEGS [42]) and four RGBD saliency methods designed especially for RGBD images (LMH [12], ACSD [13], GP [14], and LBE [15]).”
Non-local Deep Features for Salient Object Detection (2017, methodology): “…Saliency (GS) [46], Manifold Ranking (MR) [49], optimized Weighted Contrast (wCtr*) [52], Background based Single-layer Cellular Automata (BSCA) [38], Local Estimation and Global Search (LEGS) [43], Multi-Context (MC) [50], Multiscale Deep Features (MDF) [25] and Deep Contrast Learning (DCL)…”
Review of Visual Saliency Detection With Comprehensive Information (2018, methodology): “For the unsupervised RGB saliency detection methods (BSCA [25] and SMD [29]), some backgrounds”
Learning to Detect Salient Objects with Image-Level Supervision (2017, methodology): “Unsupervised Weakly Fully MR wCtr MBS BSCA WSS LEGS MDF MC DS SELD DCL RFCN ECSSD 0.690 0.676 0.673 0.705 0.823 0.785 0.807 0.796 0.826 0.810 0.829 0.834 SED 0.782 0.786 0.776 0.756 0.838 0.800 0.795 0.817 0.794 0.815 0.825 0.813 PASAL-S 0.583 0.597 0.604 0.597 0.720 – 0.705 0.687 0.655 0.714 0.710 ”
Learning Uncertain Convolutional Features for Accurate Saliency Detection (2017, methodology): “Although significant progress has been made [15, 1, 30, 21, 33, 46], saliency detection remains very challenging due to complex factors in real world scenarios.”
Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks (2017, methodology): “Therefore, we produce the final segmentation map by leveraging a parallel integration approach based on cellular automata (CA) [42]–[44].”
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].”
A Bi-Directional Message Passing Model for Salient Object Detection (2018, methodology): “We compare the proposed saliency detection model with 13 state-of-the-art methods, including 11 deep learning based methods (LEGS [27], MDF [15], RFCN [29], ELD [12], DCL [16], DHS [18], NLDF [20], DSS [8], Amulet [37], UCF [38], SRM [30]) and 2 conventional ones (DRFI [9] and BSCA [24]).”
UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory (2016, background): “MF SF [80] 0.493 GC [18] 0.539 DRFI [45] 0.690 PISA [101] 0.660 BSCA [83] 0.666 LEGS [102] 0.752 MC [112] 0.740 MDF [57] 0.764 FCN [58] 0.793 DCL [58] 0.815 DCL + CRF [58] Surface Normal Estimation: For this task surface normals are typically estimated from point cloud data, rather than directly…”
R³Net: Recurrent Residual Refinement Network for Saliency Detection (2018, background): “Among them, MR [Yang et al., 2013], wCtr* [Zhu et al., 2014] and BSCA [Qin et al., 2015] use hand-crafted features to differentiate the salient objects from background while others are based on the deep learning framework to learn the features for saliency detection.”
RGB-D salient object detection: A survey (2020, background): “[161], CLC [162], MDC [163], MIL [164], and WFD [165], to first detect salient objects in various images and then group these images into different categories (e.”
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection (2015, methodology): “To demonstrate the robustness of our method, we used some state-of-the-art single-image saliency methods [21, 24, 37, 44, 45] for initialization and reported the performance after the initialization, the learning, and the smoothing processes, respectively.”
Visual Saliency Detection Based on Multiscale Deep CNN Features (2016, background): “…multi-context deep learning (MC) [42], local estimation and global search based deep network (LEGS) [40], single-layer cellular automata (BSCA) [54], pixelwise image saliency aggregating (PISA) [55], discriminative regional feature integration (DRFI) [14], optimized weighted contrast (wCtr ∗ )…”