Name
Abstract | ||
|---|---|---|
Background modeling is an essential and important part of many high-level video processing applications. Recently, the Support Vector Data Description (SVDD) has been introduced for novelty detection when only one class of data is available, i.e. background pixels. This paper proposes a method to efficiently train an SVDD and compares the performance of this training algorithm with the traditional SVDD training techniques. We compare the performance of our method with traditional SVDD and other classification algorithms on various data sets including real video sequences. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761328 | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
Keywords | Field | DocType |
optimization,object recognition,pixel,data models,computational modeling,video processing,support vector machines,kernel,learning artificial intelligence | Data modeling,Novelty detection,Data set,Computer science,Artificial intelligence,Kernel (linear algebra),Computer vision,Object detection,Video processing,Pattern recognition,Support vector machine,Statistical classification,Machine learning | Conference |
ISSN | Citations | PageRank |
1051-4651 | 4 | 0.69 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alireza Tavakkoli | 1 | 168 | 15.97 |
| Mircea Nicolescu | 2 | 792 | 55.76 |
| George Bebis | 3 | 2397 | 149.44 |
| Monica N. Nicolescu | 4 | 358 | 40.44 |