Multiple-instance ranking: Learning to rank images for image retrieval

By Yang Hu, Mingjing Li, Nenghai Yu. 2008

80 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning Theory for Distribution Regression (2014, background): “…2009; Kwok and Cheung, 2007), (ii) clustering (Zhang and Zhou, 2009; Zhang et al., 2009, 2011; Chen and Wu, 2012), (iii) ranking (Bergeron et al., 2008; Hu et al., 2008; Bergeron et al., 2012), (iv) outlier detection (Wu et al., 2010), (v) transfer learning (Raykar et al., 2008; Zhang and Si,…”
Ranking consistency for image matching and object retrieval (2014, methodology): “The second group of methods usually use pairwise ranking method, e.g. Ranking SVM [17], to sort the document (or image) relationship to the given query [9,10].”
Relative attributes (2011, background): “Learning to rank: Learning to rank has received extensive attention in the machine learning literature [22–24], for information retrieval in general and image retrieval in particular [25, 26].”
Multiple instance learning: A survey of problem characteristics and applications (2016, background): “Ranking can be performed at the bag level [42] or at the instance level [43].”
Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection (2015, background): “There exist a broad variety of MIL alternatives, that can potentially achieve higher robustness by using more than the max-scoring variables [17, 19, 36] - but the one we propose here is very easy to implement with DCNNs, as we will describe below.”
Fast Bundle Algorithm for Multiple-Instance Learning (2012, unknown): “Ç”
Ranking Domain-Specific Highlights by Analyzing Edited Videos (2014, background): “In computer vision, researchers have adopted the ranking approach mainly for the image retrieval task [6,21].”
Image ranking and retrieval based on multi-attribute queries (2011, methodology): “Similar methods have also been proposed for ranking images, [10].”
Identifying Multi-instance Outliers (2010, unknown): “Many traditional learning tasks have been brought into multi-instance framework and yields several new machine learning issues such as multi-instance ranking [14, 17], multi-instance clustering [18, 19], etc.”