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

C. Swaney | Zendy; Anton Akusok | Zendy; Kaj Mikael Björk | Zendy; Yoan Miché | Zendy; Amaury Lendasse | Zendy

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

Author(s) -

C. Swaney,

Anton Akusok,

Kaj Mikael Björk,

Yoan Miché,

Amaury Lendasse

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.07.317

Subject(s) - computer science , task (project management) , segmentation , big data , implementation , artificial neural network , artificial intelligence , pixel , data mining , machine learning , pattern recognition (psychology) , software engineering , management , economics

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

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