Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image Based Localization in Urban Environments
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Video-rate localization in multiple maps for wearable augmented reality
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Location recognition using prioritized feature matching
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
SIFT and SURF Performance Evaluation against Various Image Deformations on Benchmark Dataset
DICTA '11 Proceedings of the 2011 International Conference on Digital Image Computing: Techniques and Applications
ORB: An efficient alternative to SIFT or SURF
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Fast image-based localization using direct 2D-to-3D matching
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A feature compression scheme for large scale image retrieval systems
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
| Hi-index | 0.00 |
Image matching in large scale environments is challenging due to the large number of features used in typical representations. In this paper we investigate methods for reducing the number of SIFT (Scale invariant feature transform) features in an image based localization application. We find that reductions of up to 59% in the number of features can result in improved performance of a naive matching algorithm for highly redundant data sets. However, those improvements do not carry over to visual bag of words, where a more moderate feature reduction (up to 16%) is often needed to maintain performance similar to the non-reduced set. Our reduced features have performed better than other robust feature descriptors namely HoG, GIST and ORB on all data sets with naive matching. The main contribution of this paper is the compact feature representation of a large scale environment for robust 2D image matching.