CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

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.Comment: 8 pages, Submitted in CCIA 201

Fine-grained food image classification and recipe extraction using a customised Deep Neural Network and NLP

Global eating habits cause health issues leading people to mindful eating. This has directed attention to applying deep learning to food-related data. The proposed work develops a new framework integrating neural network and natural language processing for classification of food images and automated recipe extraction. It address the challenges of intra-class variability and inter-class similarity in food images that have received shallow attention in the literature. Firstly, a customised lightweight deep convolution neural network model, MResNet-50 for classifying food images is proposed. Secondly, automated ingredient processing and recipe extraction is done using natural language processing algorithms: Word2Vec and Transformers in conjunction. Thirdly, a representational semi-structured domain ontology is built to store the relationship between cuisine, food item, and ingredients. The accuracy of the proposed framework on the Food-101 and UECFOOD256 datasets is increased by 2.4% and 7.5%, respectively, outperforming existing models in literature such as DeepFood, CNN-Food, Wiser, and other pre-trained neural networks

Modelo de estimativa de valores nutricionais, calóricos e glicêmicos por meio de reconhecimento de alimentos em imagens digitais

Atualmente, a disponibilização de informações alimentares e nutricionais em estabelecimentos da área de alimentação não é obrigatória. Porém, os consumidores podem, fácil e rapidamente, obter informações precisas em sites confiáveis ou por meio de aplicativos sobre a composição nutricional e calórica de alimentos. Estudos recentes mostram a importância não só do controle da ingestão diária de calorias, mas também do consumo de carboidratos, que são os nutrientes mais responsáveis por elevar o nível de glicose no sangue. O objetivo deste trabalho é propor um modelo para reconhecimento de alimentos em imagens de refeições por meio de técnicas de Processamento Digital de Imagens, possibilitando assim, a estimativa dos valores nutricionais, calóricos e glicêmicos dos alimentos identificados. Foram analisadas imagens de refeições e mediante reconhecimento, foram estimados os valores calóricos, nutricionais e glicêmicos de cada alimento identificado e da refeição. O procedimento de construção do artefato foi conduzido pelo método Design Science Research. Os resultados obtidos apontam a importância de uma segmentação eficiente por porções, para que não haja interseção ou mistura de cores dos alimentos, e de utilização de algoritmos de extração de características para que a disponibilização de informações nutricionais, calóricas e glicêmicas se torne ainda mais confiável e precisa

Denominaciones genéricas de alimentos: propuesta de un modelo de análisis y orientaciones para el diseño de sistemas de clasificación bajo un enfoque de marketing

[ES] La función básica de los sistemas de clasificación de alimentos (SCAs) es informar al mercado sobre los tipos, calidad y características de los distintos tipos de alimentos. Sin embargo, a menudo presentan carencias que confunden al consumidor (ambigüedad, similitud o complejidad en los términos empleados). La finalidad principal de este trabajo es contribuir al desarrollo de SCAs eficaces, de tal modo que los consumidores, mercados y sociedad en general se beneficien de ellos. Para ello, se desarrolla y propone un modelo para analizar la calidad de cualquier SCA y se sugieren unas orientaciones para el diseño de SCAs útiles para los consumidores. El modelo y diferentes variaciones de SCAs se han probado de forma empírica mediante un experimento en un panel de consumidores online (n=960). Se concluye que el modelo propuesto es viable; se ofrecen sugerencias en el diseño de SCAs, y se sugieren cambios en algunos SCAs vigentes.[EN]The main function of food classification systems (FCSs) is to regulate the market and inform it (consumers above all) about the different types of products and their characteristics. However, the reality is that many of these systems give rise to confusion and prevent consumers from obtaining a clear idea of them, making the purchasing process more difficult. The ultimate goal of this work is to contribute to the development of efficient FCSs so that the consumers, markets and society in general benefit from them. For that, a model to analyse the quality of any FCS is developed and proposed and also some orientations to design consumer effective FCSs are suggested. The model and different variations of FCSs were empirically tested by means of an online test-survey through a consumer panel (n=960). In conclusion, the proposed model is viable. Suggestions for the design are provided and modifications for some current FCSs.Tesis Univ. Jaén. Departamento de Organización de Empresas, Marketing y Sociología. Leída el 19 de enero de 201