Image Retrieval on Large Scale Image Databases
Nowadays there exist online image repositories containing hundreds of millions of images of all kinds of quality, size and content.
These image repositories grow day by day making techniques for navigating, indexing, and searching prudent. Currently indexing is mainly based on manually entered tags and/or individual and group usage patterns. Manually entered tags, however, are very subjective and not necessarily referring to the shown image content. This subjectivity and ambiguity of tags makes image retrieval based on manually entered tags difficult.
In this project we employ the image content as the source of information to retrieve images and study the representation of images by topic models. The developed approaches are evaluated on real world, large scale image databases.
Rainer Lienhart, Eva Hörster, Stefan Romberg. Multilayer pLSA for Multimodal Image Retrieval. ACM International Conference on Image and Video Retrieval (CIVR 2009), July 8-10, 2009.
Also Technical Report 2009-02, University of Augsburg, Institute of Computer Science Apr. 2009 [ PDF ]
Eva Hörster, Rainer Lienhart and Malcolm Slaney. Image Retrieval on Large-Scale Image Databases. ACM International Conference on Image and Video Retrieval (CIVR) 2007 pp. 17-24, Amsterdam, Netherlands, July 2007.
Also Technical Report Apr. 2007 [ PDF ]
Eva Hörster and Rainer Lienhart. Fusing Local Image Descriptors for Large-Scale Image Retrieval. International Workshop on Semantic Learning Applications in Multimedia (SLAM), Minneapolis, USA, June 2007. also as Technical Report [ PDF ]
Rainer Lienhart and Malcolm Slaney. PLSA on Large Scale Image Databases. IEEE International Conference on Acoustics, Speech and Signal Processing 2007 (ICASSP 2007), Hawaii, USA, April 2007. Also Technical Report Dec. 2006 [ PDF ]