Name
Abstract | ||
|---|---|---|
Interest points have been used as local features with success in many computer vision applications such as image/video retrieval and object recognition. However, a major issue when using this approach is a large number of interest points detected from each image and created a dense feature space. This influences the processing speed in any runtime application. Selecting the most important features to reduce the size of the feature space will solve this problem. Thereby this raises a question of what makes a feature more important than the others? In this paper, we present a new technique to choose a subset of features. Our approach differs from others in a fact that selected feature is based on the context of the given image. Our experimental results show a significant reduction rate of features while preserving the retrieval performance. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISDA.2009.25 | ISDA |
Keywords | Field | DocType |
interest point,retrieval performance,important feature,computer vision application,selected feature,dense feature space,context-based adaptive filtering,video retrieval,image retrieval,feature space,local feature,interest points,pixel,data mining,set theory,computer vision,object recognition,probability density function,adaptive filter,feature extraction,adaptive filters,detectors | Computer vision,Feature vector,Automatic image annotation,Feature detection (computer vision),Pattern recognition,Feature (computer vision),Computer science,Image retrieval,Feature extraction,Artificial intelligence,Visual Word,Cognitive neuroscience of visual object recognition | Conference |
ISSN | Citations | PageRank |
2164-7143 | 2 | 0.41 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giang P. Nguyen | 1 | 53 | 5.98 |
| Hans Jørgen Andersen | 2 | 167 | 19.41 |