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Automated Design of a Computer Vision System for Visual Food Quality Evaluation

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Abstract

Considerable research efforts in computer vision applied to food quality evaluation have been developed in the last years; however, they have been concentrated on using or developing tailored methods based on visual features that are able to solve a specific task. Nevertheless, today’s computer capabilities are giving us new ways to solve complex computer vision problems. In particular, a new paradigm on machine learning techniques has emerged posing the task of recognizing visual patterns as a search problem based on training data and a hypothesis space composed by 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 features and a bank of classifiers those features and classifiers that achieve the highest performance. We tested our framework on eight different food quality evaluation 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.

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Notes

  1. This problem is known in the literature as full model selection (Escalante et al. 2009).

  2. A previous version of this paper can be found in Mery and Soto (2008) where preliminary results are presented.

  3. In our framework, other methods were implemented, however, their performance and their computational time were not satisfactory in our experiments. The other methods are: Forward Orthogonal Search (Wei and Billings 2007), Least Square Estimation (Mao 2005), Combination with Principal Components, (Duda et al. 2001), Future Selection based in Mutual Information (Peng 2005).

  4. In our framework, other methods were implemented; however, their performance and their computational time were not satisfactory in our experiments. The other methods are minimal distance (Duda et al. 2001), quadratic discriminat analysis (Hastie et al. 2001; Webb 2005), boosting and AdaBoost (Polikar 2006; Viola and Jones 2004), and probabilistic neural networks (MathWorks 2009; Duda et al. 2001).

  5. The number of folders v can be another number, for instance, 5- or 20-fold cross-validation estimates very similar performances. In our experiments, we use 10-fold cross-validation because it has become the standard method in practical terms (Witten and Frank 2005).

  6. In order to convert RGB images into L*a*b*, we use a methodology based on a calibration step with known color charts (Leon et al. 2006).

  7. No L*a*b* information was available.

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Acknowledgements

This work was supported partially by grant no. R0308LAC003 from LACCIR Virtual Institute and grant no. 1070031 from FONDECYT, Chile. Blueberry images were provided by G. Leiva, G. Mondragón, and C. Arrieta from Pontificia Universidad Catolica de Chile. Corn Tortilla images were provided by J.J. Chanona-Pérez, N. Veléz-Rivera, I. Arzate-Vázquez, and G.F. Gutiérrez-López from Instituto Politécnico Nacional de México.

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Authors and Affiliations

  1. Machine Intelligence Group ASIS-UC Interdisciplinary Research Program on Tasty and Healthy Foods, Department of Computer Science, Pontificia Universidad Catolica de Chile, Av. Vicuña Mackenna 4860, Santiago, Chile

    Domingo Mery

  2. ASIS-UC Interdisciplinary Research Program on Tasty and Healthy Foods, Department of Chemical Engineering and Bioprocesses, Pontificia Universidad Catolica de Chile, Santiago, Chile

    Franco Pedreschi

  3. Machine Intelligence Group, Department of Computer Science, Pontificia Universidad Catolica de Chile, Santiago, Chile

    Alvaro Soto

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  1. Domingo Mery
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Correspondence to Domingo Mery.

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Mery, D., Pedreschi, F. & Soto, A. Automated Design of a Computer Vision System for Visual Food Quality Evaluation. Food Bioprocess Technol 6, 2093–2108 (2013). https://doi.org/10.1007/s11947-012-0934-2

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