Salient object detection by composition

By Jie Feng, Yichen Wei, Litian Tao, Chao Zhang. 2011

166 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

What Makes for Effective Detection Proposals? (2015, background): “Methods that operate on videos and require temporal information (e.g. [33]) are considered outside the scope of this work.”
Salient Object Subitizing (2015, unknown): “Salient object detection aims at localizing salient objects in a scene by a foreground mask [1, 13] or bounding boxes [35, 23, 21, 48].”
Salient object detection: A survey (2014, background): “oxes, and algorithms that are closely related to saliency detection. Some subsections offer a different categorization of some 8 Salient Object Detection: A Survey 9 # Model Pub Year Type Code 1 COMP [57] ICCV 2011 Localization NA 2 GSAL [205] CVPR 2012 Localization NA 3 CTXT [203] ICCV 2011 Segment”
Harvesting Mid-level Visual Concepts from Large-Scale Internet Images (2013, methodology): “The scheme is inspired by the following observations: (1) search engines like Google and Bing have a massive number of well organized images; (2) using text-based queries, such as “bike”, “bird”, “tree”, allows us to crawl images of high relevance, good quality, and large diversity (at least for the”
Action Localization with Tubelets from Motion (2014, background): “This class of approaches was shown successful for salient object detection [11], weakly supervised object localization [6], and supervised object detection [29].”
Mining And-Or Graphs for Graph Matching and Object Discovery (2015, methodology): “Object discovery & co-segmentation: Operation 1 in each model-mining iteration can be regarded as a process of object discovery; therefore, we compared the object discovery performance between the mined AoG and six recent methods for object discovery and co-segmentation: saliency-guided multiple cla”
Salient Object Detection: A Benchmark (2015, unknown): “[22] ICCV/2011 - 24 Deng and Luo [39] OE/2011 - 25 Lu et al.”
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey (2018, background): “Similarly, the work in [66] defined objectness as window saliency, which is the cost for composing the window by using the remaining parts of the image.”
Prime Object Proposals with Randomized Prim's Algorithm (2013, methodology): “Following previous work [2, 3, 9, 14, 21, 27], we have used the PASCAL VOC 2007 [10] data set to evaluate our approach and compare it to the state of the art.”
Salient Object Detection: A Discriminative Regional Feature Integration Approach (2013, background): “Salient object detection by composition [13] checks if the content within an window can be composed by neighbor regions.”
Geodesic Saliency Using Background Priors (2012, background): “It has also been shown that the low level saliency cues are helpful to find generic objects irrespective to their categories [19, 20].”
Instance-Level Salient Object Segmentation (2017, methodology): “ting from multiple levels of segmentation [3,43]. Though they have been widely used as a foregoing step for object detection, they are not tailored for salient object localization. Though Feng et al. [16] proposed to generate a ranked list of salient object proposals, the overall quality of their re”
Online Video SEEDS for Temporal Window Objectness (2013, unknown): “In this way, we are able to compare it to s-o-a objectness measures [1, 11, 6, 14].”
Unconstrained Salient Object Detection via Proposal Subset Optimization (2016, methodology): “Some methods generate a ranked list of bounding box candidates for salient objects [21, 43, 52], but they lack an effective way to fully answer the questions of Existence and Localization.”
Contextual Hypergraph Modeling for Salient Object Detection (2013, background): “Recently, a large body of work concentrates on salient object detection [4–17], whose goal is to discover the most salient and attention-grabbing object in an image.”
Unsupervised object class discovery via saliency-guided multiple class learning (2012, methodology): “White rectangles: saliency detection results by [12].”
Saliency Detection via Graph-Based Manifold Ranking (2013, background): “Salient object detection algorithms usually generate bounding boxes [7, 10], binary foreground and background segmentation [12, 23, 24, 32], or saliency maps which indicate the saliency likelihood of each pixel.”
Contextual Hypergraph Modelling for Salient Object Detection (2013, background): “Recently, a large body of work concentrates on salient object detection [4–17], whose goal is to discover the most salient and attention-grabbing object in an image.”