Content-based visual search learned from social media

By Xirong Li, Cees G. M. Snoek, Marcel Worring. ACMMR, 2012

309 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/89935b8607eee877434d4a3f7485e04914060fa4

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

1. Dacheng Tao
2. Luc Van Gool
3. Xuelong Li
4. Jinhui Tang
5. Tao Mei
6. Yi Yang
7. Cordelia Schmid
8. Meng Wang

Citations in the corpus

• Classifying tag relevance with relevant positive and negative examples (2013, methodology): “In fact, per-tag modeling has been challenged by [6], due to its quest for many well labeled training examples and the inefficiency in applying models for many tags.”
• Deep Collaborative Embedding for Social Image Understanding (2019, methodology): “A neighbor voting strategy is developed to learn the relevance between images and tags for TBIR in [38].”
• Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search (2013, methodology): “In this section, we briefly introduce the related work on social image search and hypergraph learning.”
• Web-Based Classifiers for Human Action Recognition (2012, background): “There are also a number of studies that exploit a Web search engine or Flickr for image annotation [25], [26].”