Efficient Skin Segmentation via Neural Networks: HP-ELM and BD-SOM

C. Swaney , Anton Akusok, Kaj-Mikael Björk, Yoan Miche, Amaury Lendasse

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Peer-reviewed scientific journalProcedia Computer Science
Pages (from-to)400-409
Number of pages10
Publication statusPublished - 10.08.2015
MoE publication typeA1 Journal article - refereed


  • 512 Business and Management
  • SOM
  • ELM
  • Big Data
  • Image Processing
  • Skin Detection


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