Faculty of Informatics Vienna University of Technology Institute of Computer Aided Automation PRIP Home PRIP Home
Personal tools
You are here: Home Teaching PRIP-Talks Julian Stöttinger: Lonely but Attractive: Sparse Color Salient Points for Object Retrieval and Categorization
Navigation
 

Julian Stöttinger: Lonely but Attractive: Sparse Color Salient Points for Object Retrieval and Categorization

Test Talk

What
  • Presentation
When Jun 09, 2009
from 04:15 pm to 04:35 pm
Where Sem 183/2
Add event to calendar vCal
iCal
Local image descriptors computed in areas around salient points in images are essential for many algorithms in computer vision. Recent work suggests using as many salient points as possible. While sophisticated classifiers have been proposed to cope with the resulting large number of descriptors, processing this large amount of data is computationally costly.

In this presentation, computational methods are proposed to compute salient points designed to allow a reduction in the number of salient points while maintaining state of the art performance in image retrieval and object recognition applications. To obtain a more sparse description, a color salient point and scale determination framework is proposed operating on color spaces that have useful perceptual and saliency properties. This allows for the necessary discriminative points to be located, allowing a significant reduction in the number of salient points and obtaining an invariant (repeatability) and discriminative (distinctiveness) image description.

Experimental results on large image datasets show that the proposed method obtains state of the art results with the number of salient points reduced by half. This reduction in the number of points allows subsequent operations, such as feature extraction and clustering, to run more efficiently. It is shown that the method provides less ambiguous features, a more compact description of visual data, and therefore a faster classification of visual data.
Document Actions