Visual attention detection in video sequences using spatiotemporal cues

By Yun Zhai, Mubarak Shah. ACM Multimedia, 2006

1,071 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1b199f60d40d258e8848fa6c3f46e3ffc7aee96e

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In Computer Vision and Pattern Recognition

Citations in the corpus

State-of-the-Art in Visual Attention Modeling (2013, methodology): “In [103], a spatio-temporal attention modeling approach for videos is presented by combining motion contrast derived from the homography between two images and spatial contrast calculated from color histograms.”
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”
Geodesic Saliency Using Background Priors (2012, methodology): “Recall P re ci si on FT [9] HC [12] LC [33] RC [12] SR [21] IT [1] GB [22] CA [11] GS_GD GS_SP”
Saliency filters: Contrast based filtering for salient region detection (2012, background): “We compare to spectral residual saliency (SR [16]), fuzzy growing (MZ [22]), spatiotemporal cues (LC [29]), visual attention measure (IT [18]), graph-based saliency (GB [15]), salient region detection (AC [1]), context-aware saliency (CA [12]), frequency-tuned saliency (FT [2]) and global-contrast s”
GrabCut in One Cut (2013, background): “Saliency segmentation results reported for dataset [1]: Precison-Recall and F-measure bars for E3(S), E4(S) are compared to FT[1], CA[9], LC[24], HC[7] and RC[7].”
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.”
TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2021, methodology): “Saliency map [35] is utilized to get the most informative part of the EEG data identified by the network.”
Efficient Salient Region Detection with Soft Image Abstraction (2013, methodology): “Initial efforts tried to adopt only luminance [50] or first-order average color [2] to effectively estimate consistent results.”
New Contour Cue-Based Hybrid Sparse Learning for Salient Object Detection (2019, methodology): “…for simplicity 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],…”
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (2015, background): “Video anomaly detection is also important as it is related to other interesting topics in computer vision, such as dominant behavior detection [23], visual saliency [37] and interestingness prediction [7].”
Making 360° Video Watchable in 2D: Learning Videography for Click Free Viewing (2017, background): “Although the research originated in static images, there is increasing work that studies video saliency [21, 35, 43].”
Hierarchical Saliency Detection (2013, methodology): “Zhai and Shah [31] introduced image histograms to calculate color saliency.”
Region-Based Saliency Detection and Its Application in Object Recognition (2014, background): “Zhai and Shah measured the global contrast of a pixel by comparing it with all the other pixels in the image.”
Attentive Systems: A Survey (2017, background): “…et al. 2015), q high-dimensional color transform (HDCT, Kim et al. 2014), r hierarchical saliency (HS, Yan et al. 2013), s spatial temporal cues (LC, Zhai and Shah 2006), and t saliency filters (SF, Perazzi et al. 2012) Generic saliency models yield a saliency map that later is utilized by some…”
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].”
Surface defect saliency of magnetic tile (2018, methodology): “The 14 models in the toolbox are: ITTI [30] and BMS [14] based on visual-attention mechanism; FT [28], LC [46] and HC [47] based on global color rarity; AC [34] and MSS [29] based on local color rarity; SR [48], Rudinac [49] and PHOT [36] based on frequency domain; RC [47], SF [50] and GMR [26]…”
Salient Region Detection by UFO: Uniqueness, Focusness and Objectness (2013, methodology): “Zhai and Shah [40] compute pixel-level saliency using the luminance information.”