Local Features for Image Retrieval
State-of-the-Art in Content-Based Image and Video Retrieval [Dagstuhl Seminar, 5-10 December 1999]
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A tutorial on spectral clustering
Statistics and Computing
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of Image SIFT Features to the Context of CBIR
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
Object Mining Using a Matching Graph on Very Large Image Collections
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
Document retrieval using image features
Proceedings of the 2010 ACM Symposium on Applied Computing
Content-based image retrieval: an application to tattoo images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Building Rome on a cloudless day
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Name that room: room identification using acoustic features in a recording
Proceedings of the 20th ACM international conference on Multimedia
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We address scene-based image retrieval, the challenge of finding pictures taken at the same location as a given query image, whereas a key challenge lies in the fact that target images may show the same scene but different parts of it. To overcome this lack of direct correspondences with the query image, we study two strategies that exploit the structure of the targeted image collection: first, cluster matching, where pictures are grouped and retrieval is conducted on cluster level. Second, we propose a probabilistically motivated shortest path approach that determines retrieval scores based on the shortest path in a cost graph defined over the image collection. We evaluate both approaches on several datasets including indoor and outdoor locations, demonstrating that the accuracy of scene-based retrieval can be improved distinctly (by up to 40%), particularly by the shortest path approach.