Automated Design of a Computer Vision System for Food Quality Evaluation

Automated Design of a Computer Vision System for Food Quality Evaluation. Mery, D., Pedreschi, F., & Soto, A. Food and Bioprocess Technology, 6(8):2093-2108, 2013.

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Considerable research efforts in computer classifiers for a given application avoiding the classical vision applied to food quality evaluation have been trial and error framework commonly used by human developed in the last years; however, they have been designers. The key idea of the proposed framework concentrated on using or developing tailored methods is to select—automatically—from a large set of fea- based on visual features that are able to solve a specific tures and a bank of classifiers those features and clas- task. Nevertheless, today’s computer capabilities are sifiers that achieve the highest performance. We tested giving us new ways to solve complex computer vision our framework on eight different food quality evalua- problems. In particular, a new paradigm on machine tion problems yielding a classification performance of learning techniques has emerged posing the task of 95 % or more in every case. The proposed framework recognizing visual patterns as a search problem based was implemented as a Matlab Toolbox available for on training data and a hypothesis space composed by noncommercial purposes. visual features and suitable classifiers. Furthermore, now we are able to extract, process, and test in the same time more image features and classifiers than before. Thus, we propose a general framework that designs a computer vision system automatically, i.e., it finds— without human interaction—the features and the classifiers for a given application avoiding the classical trial and error framework commonly used by human designers. The key idea of the proposed framework is to select—automatically—from a large set of fea- tures and a bank of classifiers those features and clas- sifiers that achieve the highest performance. We tested our framework on eight different food quality evalua- tion problems yielding a classification performance of 95% or more in every case. The proposed framework was implemented as a Matlab Toolbox available for noncommercial purposes.

@Article{	  mery:etal:2013,
  author	= {D. Mery and F. Pedreschi and A. Soto},
  title		= {Automated Design of a Computer Vision System for Food
		  Quality Evaluation},
  journal	= {Food and Bioprocess Technology},
  volume	= {6},
  number	= {8},
  pages		= {2093-2108},
  year		= {2013},
  abstract	= {Considerable research efforts in computer classifiers for
		  a given application avoiding the classical vision applied
		  to food quality evaluation have been trial and error
		  framework commonly used by human developed in the last
		  years; however, they have been designers. The key idea of
		  the proposed framework concentrated on using or developing
		  tailored methods is to select—automatically—from a
		  large set of fea- based on visual features that are able to
		  solve a specific tures and a bank of classifiers those
		  features and clas- task. Nevertheless, today’s computer
		  capabilities are sifiers that achieve the highest
		  performance. We tested giving us new ways to solve complex
		  computer vision our framework on eight different food
		  quality evalua- problems. In particular, a new paradigm on
		  machine tion problems yielding a classification performance
		  of learning techniques has emerged posing the task of 95 %
		  or more in every case. The proposed framework recognizing
		  visual patterns as a search problem based was implemented
		  as a Matlab Toolbox available for on training data and a
		  hypothesis space composed by noncommercial purposes. visual
		  features and suitable classifiers. Furthermore, now we are
		  able to extract, process, and test in the same time more
		  image features and classifiers than before. Thus, we
		  propose a general framework that designs a computer vision
		  system automatically, i.e., it finds— without human
		  interaction—the features and the classifiers for a given
		  application avoiding the classical trial and error
		  framework commonly used by human designers. The key idea of
		  the proposed framework is to select—automatically—from
		  a large set of fea- tures and a bank of classifiers those
		  features and clas- sifiers that achieve the highest
		  performance. We tested our framework on eight different
		  food quality evalua- tion problems yielding a
		  classification performance of 95% or more in every case.
		  The proposed framework was implemented as a Matlab Toolbox
		  available for noncommercial purposes.
		  
		  },
  url		= {http://saturno.ing.puc.cl/media/papers_alvaro/Food-Mery-2012.pdf}
}

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