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An ensemble feature method for food classification

Martinel N., Piciarelli C., Micheloni C.

Machine Graphics and Vision

2017

Vol. 26, No. 1/4

13--39

In the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classiﬁcation. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this speciﬁc task. In this work, we adopt a Random Forest classiﬁer to measure the performances of diﬀerent texture ﬁlter banks and feature encoding techniques on three diﬀerent food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classiﬁers with other feature-based state-of-the-art solutions.