Name
Abstract | ||
|---|---|---|
Today, with the higher computing power of CPUs and GPUs, many different neural network architectures have been proposed for object detection in images. However, these networks are often not optimized to process color information. In this paper, we propose a new method based on an SVM network, that efficiently extracts this color information. We describe different network archi-tectures and compare them with several color models (CIELAB, HSV, RGB...). The results obtained on real data show that our network is more efficient and robust than a single SVM network, with an average precision gain ranging from 1.5% to 6% with respect to the complexity of the test image database. We have optimized the network architecture in order to gain information from color data, thus increasing the average precision by up to 10%. |
Year | Venue | Field |
|---|---|---|
2016 | Visual Information Processing and Communication | Object detection,Computer vision,HSL and HSV,Information processing,Computer science,Support vector machine,Network architecture,Color model,Artificial intelligence,Artificial neural network,Standard test image |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jerome Pasquet | 1 | 24 | 3.20 |
| Gérard Subsol | 2 | 393 | 84.30 |
| Mustapha Derras | 3 | 9 | 9.98 |
| Marc Chaumont | 4 | 172 | 20.40 |