Social Multimedia computing is a new approach which combines the contextual information available in the social networks with available multimedia content to achieve greater accuracy in traditional multimedia problems like face and landmark recognition. Tian et al. introduce this concept and suggest various fields where this approach yields significant benefits. In this paper, this approach has been applied to the landmark recognition problem. The dataset of flickr.com was used to select a set of images for a given landmark. Then image processing techniques were applied on the images and text mining techniques were applied on the accompanying social metadata to determine independent rankings. These rankings were combined using models similar to meta search engines to develop an improved integrated ranking system. Experiments have shown that the recombination approach gives better results than the separate analysis.
|Original language||English (US)|
|Title of host publication||Advances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Proceedings|
|Editors||Kuo-Tien Lee, Jun-Wei Hsieh, Wen-Hsiang Tsai, Hong-Yuan Mark Liao, Tsuhan Chen, Chien-Cheng Tseng|
|Number of pages||11|
|State||Published - 2011|
|Event||17th International Conference on Multimedia Modeling, MMM 2011 - Taipei, Taiwan, Province of China|
Duration: Jan 5 2011 → Jan 7 2011
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||17th International Conference on Multimedia Modeling, MMM 2011|
|Country/Territory||Taiwan, Province of China|
|Period||1/5/11 → 1/7/11|
Bibliographical noteFunding Information:
We would like to express our gratitude to members of the DMR lab in the University of Minneosta, for their valuable inputs during both design and discussion phase.This work is supported by a grant from the Chinese National Natural Science Foundation under contract number 60973055,a grant from the CADAL project and ARL Network Science CTA via BBN TECH/W911NF-09-2-0053.
© Springer-Verlag Berlin Heidelberg 2011.
- Landmark recognition
- Social mutimedia computing