Abstract
This Paper Presents Two Novel Methods for Skin Detection: Hp-Elm and Bd-Som. Both Som and Elm Are Fast for Large Data Sets, But Not Yet Suitable for Big Data. We Show How They Can Be Improved in Order to Fulfill the Strict Requirements for Big Data. Both New Methods Are Described and their Implementations Are Explained. a Comparison on a Large Example is Presented in the Experiment Section. We Find that Bd-Som is More Accurate but Not as Computationally Efficient as Hp-Elm. as a Result, We Show that Both Methods Work Well on a Big Data Task. the Given Task Deals with the Classification of More Than One Billion Samples (Pixels) between Skin and Non-Skin Categories.
Recommended Citation
C. Swaney et al., "Efficient Skin Segmentation Via Neural Networks: HP-ELM and BD-SOM," Procedia Computer Science, vol. 53, no. 1, pp. 400 - 409, Elsevier, Jan 2015.
The definitive version is available at https://doi.org/10.1016/j.procs.2015.07.317
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Big Data; ELM; Image processing; Skin detection; SOM
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 2015
