Fusing generic objectness and visual saliency for salient object detection

By Kai-Yueh Chang, Tyng-Luh Liu, Hwann-Tzong Chen, Shang-Hong Lai. 2011

390 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Sensory Systems

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Citations in the corpus

What Makes for Effective Detection Proposals? (2015, background): “The contributions of this work are as follows: • In §2 we provide a systematic overview of detection proposal methods and define simple baselines that serve as reference points.”
Saliency Detection via Graph-Based Manifold Ranking (2013, methodology): “Method Ours CB [18] Gof [11] SVO [7] Time(s) 0.”
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].”
Non-local Deep Features for Salient Object Detection (2017, background): “Prior efforts have tested simple features such as color and grayscale [2], edges [13] or texture [10], as well as more complex features such as objectness, focusness and backgroundness [18, 46, 9].”
Visual Saliency Detection Based on Multiscale Deep CNN Features (2016, methodology): “Top-down methods in general require the incorporation of high-level knowledge, such as objectness and object detectors in the computational process [31], [32], [29].”
RGBD Salient Object Detection: A Benchmark and Algorithms (2014, methodology): “We select 12 state-of-the-art RGB saliency detection approaches, including the top 4 models ranked in the survey[6]: SVO[9], CA[14], CB[20], and RC[10], and 8 recently developed prominent methods: SF[34], LR[40], HS[44], MR[45], PCA[32], TD[37], GC[11], and DSR[28].”
Background Prior-Based Salient Object Detection via Deep Reconstruction Residual (2015, methodology): “These approaches include SVO [37], RC [17], CBS [38], CNTX [33], GS-G [18], GS-S [18], BLSM [39], PD [34], and GBMR [19].”
Salient object detection via bootstrap learning (2015, methodology): “We first evaluate the proposed algorithms and other 19 state-of-the-art methods including the IT98 [16], SF [28], LRMR [30], wCO [43], GS SP [34], XL13 [36], RA10 [29], GB [12], LC [41], SR [15], FT [1], CA [11], SVO [8], CBsal [18], GMR [38], GC [10], HS [37], RC-J [9] and DSR [23] methods on the A”
Contextual Hypergraph Modeling for Salient Object Detection (2013, methodology): “In the experiments, we qualitatively and quantitatively compare the proposed approach with twelve state-of-theart approaches, including GS SP [5], LR [12], SF [11], CB [13], SVO [15], RC [7], HC [7], RA [16], FT [4], CA [14], ICL [3], and IT [1].”
New Contour Cue-Based Hybrid Sparse Learning for Salient Object Detection (2019, methodology): “…we denote as BMS [10], CA [52], CB [35], CGVS [53], CRF [15], DSR [20], FES [54], FT [16], GMR [30], GR [36], GS [28], HS [55], LC [56], LEGS [42], LMLC [41], LPS [57], MBS (MBS+) [45], MC [29], MNP [58], PSO [59], RC [17], SEG [60], SeR [61], SF [49], ST [62], and SVO [34], respectively.”
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection (2016, neutral): “Objectness prior [13, 14] tends to highlight an”
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey (2018, background): “[68] proposed to integrate the objectness prior (including object size and location) and saliency prior to detect salient objects via a unified graphical model.”
Hierarchical Image Saliency Detection on Extended CSSD (2016, background): “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.”
Salient Region Detection by UFO: Uniqueness, Focusness and Objectness (2013, methodology): “The objectness is later used popularly in various vision tasks such as object detection [6] and image retargeting [32].”
Saliency Detection via Absorbing Markov Chain (2013, neutral): “MSRA: On the MSRA dataset, we compare the proposed method with eleven state-of-the-art methods which are LR [29], CB [17], SVO [7], RC [8], CA [27], SER [31], FT [2], GB [14], SR [15], LC [37] and IT [16].”
Saliency Detection via Dense and Sparse Reconstruction (2013, background): “Original IT [13] GB [10] SR [11] FT [2] CA [9] RA [18] DW [8] CB [14] RC [7] SVO [6] LR [19] DSR DSR cut GT”
Salient object detection: A survey (2014, background): “ov Chain. Beyond these approaches, the generic objectness prior5 is also exploited to facilitate the detection of salient objects by leveraging the object proposal generation model [15]. Chang et al. [73] present a computational framework by fusing the objectness and regional saliency into a graphic”
Structure-Measure: A New Way to Evaluate Foreground Maps (2017, methodology): “art model higher than a generic map. We count the number of times a generic map scored higher than the mean score generated by the ﬁve state-ofthe-art models (CA [19], CB [23], RC [13], PCA [35], SVO [9]). The mean score provides an indication of model robustness. The results are shown in Fig.5(b). ”
We get the algorithms of our ground truths: Designing referential databases in digital image processing (2017, methodology): “Other proposed algorithms included Achanta et al. (2009); Chang et al. (2011); Goferman et al. (2012); Shen and Wu (2012); Wang et al. (2010).”
Contextual Hypergraph Modelling for Salient Object Detection (2013, methodology): “In the experiments, we qualitatively and quantitatively compare the proposed approach with twelve state-of-theart approaches, including GS SP [5], LR [12], SF [11], CB [13], SVO [15], RC [7], HC [7], RA [16], FT [4], CA [14], ICL [3], and IT [1].”