{rfName}
Cu

Indexed in

Citations

7

Altmetrics

Analysis of institutional authors

Rashwan HAuthorBanu SAuthorMoreno AAuthorPuig DAuthor

Share

November 14, 2018
Publications
>
Proceedings Paper
No

CuisineNet: Food Attributes Classification Using Multi-Scale Convolution Network

Publicated to: Fuzzy Logic-Based Variable Encoding For Improved Diabetic Retinopathy Prediction. 308 365-372 - 2018-01-01 308(), DOI: 10.3233/978-1-61499-918-8-365

Authors:

Sarker MMK; Jabreel M; Rashwan HA; Banu SF; Singh VK; Moreno A; Radeva P; Puig D
[+]

Affiliations

Universitat de Barcelona - Author
Universitat Rovira i Virgili - Author

Abstract

© 2018 The authors and IOS Press. Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.
[+]

Keywords

Cuisine recognitionDeep learningFlavor classificationFood attributes analysisPyramid pooling

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-05:

  • Google Scholar: 4
  • Scopus: 3
[+]

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-05:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 28 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

[+]

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Sarker M) and Last Author (Puig Valls, Domènec Savi).

[+]