Supervised feature selection for linear and non-linear regression of L⁎ a⁎ b⁎ color from multispectral images of meat

Sara Sharifzadeh, Line H. Clemmensen, Claus Borggaard, Susanne Stoier, Bjarne K. Ersbøll

Research output: Contribution to journalArticle

36 Citations (Scopus)
122 Downloads (Pure)

Abstract

In food quality monitoring, color is an important indicator factor of quality. The CIELab (L⁎a⁎b⁎) color space as a device independent color space is an appropriate means in this case. The commonly used colorimeter instruments can neither measure the L⁎a⁎b color in a wide area over the target surface nor in a contact-less mode. However, developing algorithms for conversion of food items images into L⁎a⁎b color space can solve both of these issues. This paper addresses the problem of L⁎a⁎b color prediction from multispectral images of different types of raw meat. The efficiency of using multispectral images instead of the standard RGB is investigated. In addition, it is demonstrated that due to the fiber structure and transparency of raw meat, the prediction models built on the standard color patches do not work for raw meat test samples. As a result, multispectral images of different types of meat samples (430–970 nm) were used for training and testing of the L⁎a⁎b prediction models. Finding a sparse solution or the use of a minimum number of bands is of particular interest to make an industrial vision set-up simpler and cost effective. In this paper, a wide range of linear, non-linear, kernel-based regression and sparse regression methods are compared. In order to improve the prediction results of these models, we propose a supervised feature selection strategy which is compared with the Principal component analysis (PCA) as a pre-processing step. The results showed that the proposed feature selection method outperforms the PCA for both linear and non-linear methods. The highest performance was obtained by linear ridge regression applied on the selected features from the proposed Elastic net (EN) -based feature selection strategy. All the best models use a reduced number of wavelengths for each of the L⁎a⁎b components.
Original languageEnglish
Pages (from-to)211-227
Number of pages17
JournalEngineering Applications of Artificial Intelligence
Volume27
Early online date16 Oct 2013
DOIs
Publication statusPublished - Jan 2014
Externally publishedYes

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence, Vol 27, (2014) DOI: 10.1016/j.engappai.2013.09.004

Keywords

  • L* a*b*color space
  • Multispectral imaging
  • Sparse regression
  • Artificial neural networks
  • Support vector machine
  • Supervised feature selection

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