Projects
Cluster-based point set saliency
Abstract
We propose a cluster-based approach to point set saliency detection, a challenge since point sets lack topological information. A point set is first decomposed into small clusters, using fuzzy clustering. We evaluate cluster uniqueness and spatial distribution of each cluster and combine these values into a cluster saliency function. Finally, the probabilities of points belonging to each cluster are used to assign a saliency to each point. Our approach detects fine-scale salient features and uninteresting regions consistently have lower saliency values. We evaluate the proposed saliency model by testing our saliency-based keypoint detection against a 3D interest point detection benchmark. The evaluation shows that our method achieves a good balance between false positive and false negative error rates, without using any topological information.
Video
Example
- Navigable version of the Bremen City data set
The dataset has 11.1M points. The default is to display 0.4M points (4% of the dataset). Use the slider at top right to change the number of points displayed.
Downloads
- Paper (PDF, 5.7 MB)
- Source: Soon
Bibtex
@inproceedings{Tasse2015, author = {Tasse, Flora P. and Kosinka, Ji{\v{r}}{\'{i}} and Dodgson, Neil A.}, title = {Cluster-based point set saliency}, year = {2015}, booktitle = {ICCV 2015}, url = {http://www.cl.cam.ac.uk/research/rainbow/projects/pointsetsaliency/}, }