Unsupervised extraction of visual attention objects in color images

By Junwei Han, King Ngi Ngan, Mingjing Li, HongJiang Zhang. IEEE transactions on circuits and systems for video technology (Print), 2006

440 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9cffbc3c72397d2bc8d3cdf7ea0d41d488e074a0

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In Computer Vision and Pattern Recognition

Citations in the corpus

Global contrast based salient region detection (2011, methodology): “[13] model color, texture, and edge features in a Markov random field framework to grow salient object regions from seed values in the saliency maps.”
Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement (2015, background): “Digital Object Identiﬁer 10.1109/TIP.2015.2460013 segmentation [35], [36], image retargeting [13], [23], [24], object recognition and detection [31], [34], [42], video compression [41] and summarization [30], [32].”
Saliency filters: Contrast based filtering for salient region detection (2012, background): “However, recent research has shown that computational models simulating low-level stimuli-driven attention [17, 20, 21] are quite successful and represent useful tools in many application scenarios, including image segmentation [14], resizing [5] and object detection [27].”
Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2015, methodology): “Here, other salient object detection models and segmentation methods can be also used for candidate positive set construction, such as the methods in Feng et al. (2011), Han et al. (2006, 2013a, 2015b,c).”
Depth saliency based on anisotropic center-surround difference (2014, background): “object segmentation [2], image classification [3, 4], image/video retargeting [5, 6], compression [7] and quality assessment [8].”
Two-Stage Learning to Predict Human Eye Fixations via SDAEs (2016, background): “The saliency models have benefited a wide range of multimedia applications such as image segmentation [2], image collage [3], image categorization [4], weakly supervised learning [5]–[7], object retrieval/detection [8]–[10], and video summarization [11], [12].”
Segmenting Salient Objects from Images and Videos (2010, background): “However, reliable saliency detection methods would be useful in many applications like adaptive compression and scaling [2, 3], unsupervised image segmentation [4, 5], and object recognition [6, 7].”
A Co-Saliency Model of Image Pairs (2011, methodology): “This model was successfully applied to object extraction from color images [11], which formulated the attention towards objects as a Markov random field (MRF) by integrating computational visual attention mechanisms with attention object growing techniques.”
Salient Region Detection and Segmentation (2008, methodology): “For instance, a Markov random field model is used to integrate the seed values from the saliency map along with low-level features of color, texture, and edges to grow the salient object regions [5].”
SalientShape: group saliency in image collections (2013, methodology): “[29] model color, texture, and edge features using a Markov random field framework and grow salient object regions from seed values in the saliency maps.”
Global Contrast Based Salient Region Detection (2015, methodology): “…wired to preferentially respond to high contrast stimulus in their receptive fields [36], we propose contrast analysis for extracting high-resolution, full-field saliency maps based on the following considerations: • A global contrast based method, which separates a large-scale object from its…”
Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining (2016, methodology): “For each image, we can obtain a set of image windows Wk with their corresponding objectness probabilities ψk , where k ∈ [1, 1000] as suggested in [40].”
Frequency-tuned salient region detection (2009, methodology): “[9] use a Markov random field to integrate the seed values from Itti’s saliency map along with low-level features of color, texture, and edges to grow the salient object regions.”
Saliency Detection: A Boolean Map Approach (2013, background): “Computing such saliency maps has recently raised a great amount of research interest (see [4] for a review) and has been shown to be beneficial in many applications, e.g. image segmentation [12], object recognition [32] and visual tracking [28].”
Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014, methodology): “These models (Achanta et al., 2008, 2009; Borji, 2012; Han et al., 2006, 2011; Harel et al., 2006; Hou and Zhang, 2007; Itti et al., 1998; Judd et al., 2009; Zhang et al., 2008) generally assume that locations in the visual field that are distinctive from their contextual background are more likely…”
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection (2020, background): “It has many useful applications, including object segmentation and recognition [27, 32, 39, 51, 70, 79], image/video compression [24], video detection/summarization [19, 41], content-based image editing [14, 23, 42, 57, 63], informative common object discovery [71,72], and image retrieval [8,22,37].”