Manhattan hashing for large-scale image retrieval
By Weihao Kong, Wu-Jun Li, Minyi Guo. 2012
109 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
- Dacheng Tao
- Jingkuan Song
- Wei David Liu
- Heng Tao Shen
- Yi Yang
- Luc Van Gool
- Jingdong Wang
- Chunhua Shen
Citations in the corpus
- Image-Based Recommendations on Styles and Substitutes (2015, background): “The system shares these characteristics with more common forms of recommender system, but does so on the basis of the appearance of the object, rather than metadata, reviews, or similar.”
- Learning to Hash for Indexing Big Data—A Survey (2015, background): “Especially, due to the well-known semantic gap, supervised and semisupervised hashing methods have been extensively studied for image search and retrieval [29], [41], [69], [129]–[131], and mobile product search [132].”
- Isotropic Hashing (2012, methodology): “The exceptions of the quantization methods only contain AGH [21], DBQ [14] and MH [15], which use two bits to quantize each dimension.”
- Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization (2014, background): “…and Fer-gus 2008; Andoni and Indyk 2008; Kulis and Darrell 2009; Lin, Ross, and Yagnik 2010; Wang, Kumar, and Chang 2010; Norouzi and Fleet 2011; Kong, Li, and Guo 2012; Heo et al. 2012; Liu et al. 2012a; Kong and Li 2012b; Norouzi, Fleet, and Salakhutdinov 2012; Strecha et al. 2012; Ge et al.…”
- Complementary Incremental Hashing With Query-Adaptive Re-Ranking for Image Retrieval (2020, unknown): “Manhattan hashing (MH) [18] encodes each hash projections with multiple hash bits for hash quantization to preserve the neighborhood structure information of data in original feature space.”
- Inter-media hashing for large-scale retrieval from heterogeneous data sources (2013, background): “Besides, various distance measures in the binarycode space, such as spherical hamming distance [14] and Manhattan distance [19] are considered to learn hash functions.”