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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 9

Position Papers of the 2016 Federated Conference on Computer Science and Information Systems

Machine Vision in Food Recognition: Attempts to Enhance CBVIR Tools

DOI: http://dx.doi.org/10.15439/2016F579

Citation: Position Papers of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 9, pages 57–61 ()

Full text

Abstract. Visual identification of complex images (e.g. images of food) remains a challenging problem. In particular, content-based visual information retrieval (CBVIR) methods, which seem a natural choice for such tasks, are often constrained by specific characteristics of the images of interest and (possibly) other practical requirements. In this paper, a novel CBVIR approach to automatic food identification is proposed, taking into account characteristics of solutions currently existing in this area. Based on limitations of those solutions, we present a scheme in which a co-occurrence of MSER features extracted from three color channels is employed to build a \textit{bag-of-words} histogram. Subsequently, food images are matched by detecting similarities between those histograms. Preliminary tests on a recently published benchmark dataset UNICT-FD889 reveal certain advantages of the scheme and highlight its limitations. In particular, a need of a novel methodology for segmentation of food images has been identified.

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