A SIFT descriptor with global context
By Eric N. Mortensen, Hongli Deng, Linda G. Shapiro. Computer Vision and Pattern Recognition, 2005
300 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/a99d2d9998ccc0545b17f6cc737a37e7e8b835d1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Mubarak Shah
- Roland Yves Siegwart
- Francis Colas
- François Pomerleau
- Haibin Ling
- Amir Zamir
- Shuicheng Yan
- Tat-seng Chua
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Dacheng Tao
- Shuicheng Yan
- Xiaogang Wang
- Jitendra Malik
- Trevor Darrell
- Yi Yang
Citations in the corpus
- Effective and Efficient Global Context Verification for Image Copy Detection (2017, methodology): “Inspired by shape context [35], [36], Mortensen et al. [37] augment SIFT feature with a high-dimensional global context vector, which describes curvilinear shape information from a much larger neighborhood.”
- An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison (2007, methodology): “Unlike the above previous work, we focus on designing a distance metric for histogram-based local descriptors, which have attracted a lot of research interests recently [3], [32], [47], [48], [26], [29], [21], [34], [31].”
- A Review of Point Cloud Registration Algorithms for Mobile Robotics (2015, background): “In [Mortensen et al., 2005], a descriptor, called Global Context, is created using surrounding feature positions.”
- Shape Classification Using the Inner-Distance (2007, background): “Some examples can be found in [33], [43], [44], [47], [34], [26].”
- Hierarchical spatio-temporal context modeling for action recognition (2009, background): “The term context is widely used in both computer vision and multimedia areas, but hitherto has been loosely defined due to its task-specific nature (see [11, 19]).”
- Featureweighting in dynamic timewarping for gesture recognition in depth data (2011, methodology): “Computer Vision are then used to detect, describe, and learn visual features [4] [6].”
- Image Geo-Localization Based on MultipleNearest Neighbor Feature Matching UsingGeneralized Graphs (2014, background): “Index Terms—Geo-location, image localization, Generalized Minimum Clique Problem (GMCP), generalized minimum spanning tree (GMST), feature matching, multiple nearest neighbor feature matching, feature correspondence, generalized graphs Ç”