Hierarchical clustering of WWW image search results using visual, textual and link information

By Deng Cai, Xiaofei He, Zhiwei Li, Wei-Ying Ma, Ji-rong Wen. MULTIMEDIA '04, 2004

390 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/68332b6a7f4a792eaeda56d84d0e4de32395411b

How researchers see this paper

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

In Artificial Intelligence

1. Guojun Lu
2. Dengsheng Zhang
3. Yibo Jiang
4. L. Kennedy
5. M. Islam
6. Mor Naaman
7. Wei-Ying Ma
8. Sourav S. Bhowmick

In Computer Vision and Pattern Recognition

1. Guojun Lu
2. Dengsheng Zhang
3. M. Islam
4. Mor Naaman
5. Yibo Jiang
6. L. Kennedy
7. Wei-Ying Ma
8. Sourav S. Bhowmick

Citations in the corpus

• Meta-Learning to Cluster (2019, methodology): “Clustering is one of the most ubiquitous techniques in data analysis that has important applications in numerous domains that extend far beyond machine learning [Xu et al., 2003, Cai et al., 2004, Tung et al., 2010, Xu and Tian, 2015].”
• Image retrieval: Ideas, influences, and trends of the new age (2008, neutral): “From the perspective of application, clustering specifically for Web images has received particular attention from the multimedia community, where meta-data is often available for exploitation in addition to visual features [Wang et al. 2004; Gao et al. 2005; Cai et al. 2004].”
• Iteratively clustering web images based on link and attribute reinforcements (2005, neutral): “[2] proposed a WWW image clustering approach which firstly constructs an image graph based on WWW image hyperlinks and textual annotations, and learn new image feature representations based on this graph.”
• Generating diverse and representative image search results for landmarks (2008, result): “Clustering of the results was suggested in a number of papers [4, 26].”
• PRISM: concept-preserving social image search results summarization (2014, background): “[2] exploit a combination of visual, textual, and edge information of Web images to construct a relationship graph.”
• A review on automatic image annotation techniques (2012, methodology): “A number of techniques have been developed for annotating web images, most of them integrating both metadata and visual features for accurate image annotation [49–54,66].”
• A survey of content-based image retrieval with high-level semantics (2007, methodology): “For example, by considering the application domain, they can be classified as those targeting at artwork retrieval [21], scenery image retrieval [27,28,31], WWW images retrieval [63,64], etc.”