Paper Info
Title
Skin and non-skin probability approximation based on discriminative tree distribution
Abstract
We investigate the probability tree models to approximate skin and non-skin distributions. These models have presented good results in solving the skin detection problem. However, there are two main disadvantages of the existing skin/non-skin tree distributions based models: (1) the structure of some tree distributions is predefined; and (2) the inter and the intra classes of skin/non-skin are not taken into account at the same time by the existing skin and/or non-skin tree models. To overcome these drawbacks, we propose a new classifier based on an image patch joint distribution approximation modelled by a discriminative skin/non-skin tree. On the Compaq database, we examine the performances of the proposed approach compared with the baseline model and two others based on dependency tree's distributions. Experimental results show that the new approach is a significant improvement over the others.
Year
DOI
Venue
2009
10.1109/ICIP.2009.5413486
Image Processing
Keywords
Field
DocType
non-skin tree,discriminative skin,tree distribution,existing skin,probability tree model,discriminative tree distribution,dependency tree,skin detection problem,non-skin tree model,approximate skin,non-skin probability approximation,nonskin tree distribution,databases,skin,computational modeling,pixel,probability,graphical model,approximation theory,image segmentation
Object detection,Joint probability distribution,Tree diagram,Pattern recognition,Computer science,Approximation theory,Image segmentation,Artificial intelligence,Graphical model,Classifier (linguistics),Discriminative model
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4244-5655-0
978-1-4244-5655-0
2
PageRank 
References 
Authors
0.43
8
3
Name
Order
Citations
PageRank
Sanaa El Fkihi1107.52
Mohamed Daoudi2148986.39
D. Aboutajdine39312.21