Analysis & Numerical Simulation of Indian Food Image Classification Using Convolutional Neural Network

Recognition of Indian food can be assumed to be a fine-grained visual task owing to recognition property of various food classes. It is therefore important to provide an optimized approach to segmentation and classification for different applications based on food recognition. Food computation mainly utilizes a computer science approach which needs food data from various data outlets like real-time images, social flat-forms, food journaling, food datasets etc, for different modalities. In order to consider Indian food images for a number of applications we need a proper analysis of food images with state-of-art-techniques. The appropriate segmentation and classification methods are required to forecast the relevant and upgraded analysis. As accurate segmentation lead to proper recognition and identification, in essence we have considered segmentation of food items from images. Considering the basic convolution neural network (CNN) model, there are edge and shape constraints that influence the outcome of segmentation on the edge side. Approaches that can solve the problem of edges need to be developed; an edge-adaptive As we have solved the problem of food segmentation with CNN, we also have difficulty in classifying food, which has been an important area for various types of applications. Food analysis is the primary component of health-related applications and is needed in our day to day life. It has the proficiency to directly predict the score function from image pixels, input layer to produce the tensor outputs and convolution layer is used for self- learning kernel through back-propagation. In this method, feature extraction and Max-Pooling is considered with multiple layers, and outputs are obtained using softmax functionality. The proposed implementation tests 92.89% accuracy by considering some data from yummly dataset and by own prepared dataset. Consequently, it is seen that some more improvement is needed in food image classification. We therefore consider the segmented feature of EA-CNN and concatenated it with the feature of our custom Inception-V3 to provide an optimized classification. It enhances the capacity of important features for further classification process. In extension we have considered south Indian food classes, with our own collected food image dataset and got 96.27% accuracy. The obtained accuracy for the considered dataset is very well in comparison with our foregoing method and state-of-the-art techniques.

Implementation of an IoT ecosystem for controlling calorie intake through deep learning mechanisms

Trabajo de Fin de Máster en Internet de las Cosas, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2020/2021El presente trabajo propone una solución inteligente dentro del paradigma IoT (Internet of Things). Se trata de un diseño que clasifica imágenes de alimentos capturadas con la cámara de un dispositivo móvil según el tipo de alimento a la vez que se obtienen las calorías asignadas al mismo con el fin de llevar un control sobre la ingesta de calorías en el tiempo. La solución inteligente se centra en la utilización de dos modelos de redes neuronales convolucionales, concretamente AlexNet y GoogLeNet, que se adaptan y se rediseñan de acuerdo con las especificaciones de la aplicación, más concretamente para la clasificación de diez categorías de alimentos distintas, a partir de los cuales se puede determinar su nivel de calorías. Son modelos pre-entrenados, que se re-entrenan para las imágenes propias en lo que se conoce como transferencia de aprendizaje. Los resultados de la clasificación se envían a ThingSpeak, que es una plataforma remota para almacenamiento de datos y procesamiento en la nube específicamente diseñada para aplicaciones IoT. De esta forma es posible monitorizar la trazabilidad de los datos almacenados, principalmente la ingesta de calorías. La aplicación, basada en distintos componentes de Matlab, consta de un módulo de captura de imágenes a través de la cámara de un dispositivo móvil, que tiene a la vez instalada una aplicación con capacidad de comunicación on-line, tanto con un computador central como con los servicios en la nube de Matlab (Drive) o la mencionada plataforma ThingSpeak, desde donde se pueden enviar alarmas o avisos vía Twitter. Los distintos módulos, convenientemente integrados, constituyen la aplicación en su conjunto, que permite determinar su validez mediante el análisis de los resultados obtenidos.The present project proposes an intelligent solution under the Internet of Things (IoT) paradigm. It is a design which classifies food images captured with the camera of a mobile device according to the type of food while obtaining the calories assigned to it in order to keep track of calorie intake over time. This intelligent solution focuses on the use of two convolutional neural network models, particularly AlexNet and GoogLeNet, which are adapted, redesigned according to the application specifications, those being the classification of ten different categories of food, from which the level of calories can be determined. These are pre-trained models, which are re-trained with images in what is known as transfer learning. The results of the classification are then sent to ThingSpeak, which is a remote platform for data storage and cloud processing specifically designed for IoT applications. Thus, it is possible to monitor the traceability of the stored data, mainly the calorie intake. The application, based on different Matlab components, consists of an image capturing module by using the camera of a mobile device, which also has installed an application with on-line communication capacity, both with a central computer, Matlba’s cloud services (Drive) and the aforementioned ThingSpeak platform, from which alarms or warnings can be sent via Twitter. The different modules, conveniently integrated, constitute the application as a whole, which makes it possible to determine its validity by analyzing the results obtained.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu