Generating diverse and representative image search results for landmarks

By L. Kennedy, Mor Naaman. The Web Conference, 2008

442 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geography, Planning and Development

View Paper: https://www.semanticscholar.org/paper/82e5c1534785e9b00040573b1bf35eef640d9cae

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. Yi Yang
4. Trevor Darrell
5. Xiaogang Wang
6. Ming-Hsuan Yang
7. Andrew Zisserman
8. Jitendra Malik

In Geography, Planning and Development

1. Tao Mei
2. Jiawei Han
3. Yong Rui
4. Winston H. Hsu
5. Chun-Che Wu

Citations in the corpus

• Photo2Trip: generating travel routes from geo-tagged photos for trip planning (2010, background): “Most existing research efforts on user generated content [9, 7, 2, 13] mainly focus on landmarks recognition, scene visualization and recommendation.”
• Learning to personalize trending image search suggestion (2014, methodology): “In addition, Kennedy et al. adopt a time information as criteria [16].”
• Tour the world: Building a web-scale landmark recognition engine (2009, background): “Contrasting to [8], [12] and [7], the principal focus of our approach is to explore landmarks at a world-scale.”
• Travel route recommendation using geotags in photo sharing sites (2010, background): “…spatial information into Web applications; recent research efforts have shown the potential of geotags by developing various geotag-associated applications such as georeferenced image search [7, 8], automatic image geolocation [9, 10, 11], and georeferenced content browsing [15, 16, 17, 18, 19].”
• Mapping the world's photos (2009, background): “In [11] the authors address the problem of searching a collec tion of geolocated images, using a combination of spatial, text t ag and image content features.”
• Geographical topic discovery and comparison (2011, background): “In [7], Kennedy et al.”
• Image Clustering Using Local Discriminant Models and Global Integration (2010, methodology): “In [16] and [28], the image clustering algorithms were utilized to extract a small subset of representative images for representing the diverse views of the landmarks.”