Analyzing the effect of hyperparameters in a automobile classifier based on convolutional neural networks

Elian Laura Riveros, Jose Galdos Chavez, Juan Carlos Gutierrez Caceres

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In the recent years the convolutional neural network is used successfully in applications of image classification, due to its deep and hierarchical architecture. The hyper parameters of the convolutional neural networks are of great influence to obtain good results in binary classification without the need of a large number of layers. The activation function, the weights initialization and the sub sampling function are the three main hyper parameters. In the present work 27 models of convolutional neural network are trained and tested with automobile images taken from a surveillance camera. The illumination intensity of the test images are different from the training images, because they were taken from scenes of day, evening and night. We also demonstrate the influence of the mean of the images and the size of the filter kernel. The convolutional neural network model with the best result reached 95.6% of accuracy. The results of experiments show that neural networks predict successfully automobile images with varied illumination intensities overcome the techniques Haar Cascade and the Support Vector Machine.

Original languageEnglish
Title of host publicationProceedings of the 2016 35th International Conference of the Chilean Computer Science Society, SCCC 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781509033393
DOIs
StatePublished - 27 Jan 2017
Event35th International Conference of the Chilean Computer Science Society, SCCC 2016 - Valparaiso, Chile
Duration: 10 Oct 201614 Oct 2016

Publication series

NameProceedings - International Conference of the Chilean Computer Science Society, SCCC
ISSN (Print)1522-4902

Conference

Conference35th International Conference of the Chilean Computer Science Society, SCCC 2016
Country/TerritoryChile
CityValparaiso
Period10/10/1614/10/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • automobile recognition in images
  • convolutional networks
  • image classification
  • Image processing

Fingerprint

Dive into the research topics of 'Analyzing the effect of hyperparameters in a automobile classifier based on convolutional neural networks'. Together they form a unique fingerprint.

Cite this