Name
Abstract | ||
|---|---|---|
Body pixel classification is a multiclass pixel by pixel image segmentation problem that aims to classify each image pixel to its correspondent human body part. In this article we initially adopted for this problem a Multilayer Perceptron neural network (MLP) classifier using back propagation algorithm to learn network weights and biases. Then confidence intervals based on diffMax criterion are computed in order to make classification more certain. This criterion is computed by the difference between the first and second maximum value of MLP output vector. A 92 % correct classification rate was achieved after applying confidence classification. The classification result will be integrated as an input to a human posture recognition system. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33503-7_48 | ICIRA (3) |
Keywords | Field | DocType |
body pixel classification,mlp output vector,confidence interval,confidence classification,pixel image segmentation problem,image pixel,multiclass pixel,classification result,multilayer perceptron neural network,correct classification rate | Back propagation algorithm,Computer vision,Pattern recognition,Computer science,Pixel classification,Image segmentation,Pixel,Artificial intelligence,Classifier (linguistics),Artificial neural network,Classification rate,Posture recognition | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hazar Chaabani | 1 | 0 | 0.34 |
| Wassim Filali | 2 | 4 | 1.66 |
| Thierry Simon | 3 | 3 | 2.23 |
| Frederic Lerasle | 4 | 23 | 3.52 |