Name
Abstract | ||
|---|---|---|
In this paper we study how the use of a novel model of bottom-up saliency (visual attention), based on local energy and color, can significantly accelerate scene recognition and, at the same time, preserve the recognition performance. To do so, we use a mobile robot-like application where scene recognition is performed through the use of SIFT features to characterize the different scenarios, and the Nearest Neighbor rule to carry out the classification. Experimental work shows that important reductions in the size of the database of prototypes can be achieved (17.6% of the original size) without significant losses in recognition performance (from 98.5% to 96.1%), thus accelerating the classification task. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02172-5_4 | IbPRIA |
Keywords | Field | DocType |
improving scene recognition,nearest neighbor rule,classification task,visual attention,important reduction,experimental work,bottom-up saliency,original size,scene recognition,different scenario,local energy,recognition performance,mobile robot,nearest neighbor,bottom up | k-nearest neighbors algorithm,Scale-invariant feature transform,Computer vision,Pattern recognition,Computer science,Salience (neuroscience),Visual attention,Artificial intelligence | Conference |
Volume | ISSN | Citations |
5524 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fernando López-García | 1 | 5 | 1.78 |
| Antón Garcia-Diaz | 2 | 131 | 6.78 |
| Xose Ramon Fdez-Vidal | 3 | 0 | 0.34 |
| Xose Manuel Pardo | 4 | 42 | 5.30 |
| Raquel Dosil | 5 | 145 | 10.37 |
| David Luna | 6 | 0 | 0.34 |