6 research outputs found
Exploring Food Detection using CNNs
One of the most common critical factors directly related to the cause of a
chronic disease is unhealthy diet consumption. In this sense, building an
automatic system for food analysis could allow a better understanding of the
nutritional information with respect to the food eaten and thus it could help
in taking corrective actions in order to consume a better diet. The Computer
Vision community has focused its efforts on several areas involved in the
visual food analysis such as: food detection, food recognition, food
localization, portion estimation, among others. For food detection, the best
results evidenced in the state of the art were obtained using Convolutional
Neural Network. However, the results of all these different approaches were
gotten on different datasets and therefore are not directly comparable. This
article proposes an overview of the last advances on food detection and an
optimal model based on GoogLeNet Convolutional Neural Network method, principal
component analysis, and a support vector machine that outperforms the state of
the art on two public food/non-food datasets
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.
Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model
Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and convolutional neural networks proved to be a boon for the image classification and recognition tasks, specifically for food recognition because of the wide variety of food items. In this paper, we report experiments on food/non-food classification and food recognition using a GoogLeNet model based on deep convolutional neural network. The experiments were conducted on two image datasets created by our own, where the images were collected from existing image datasets, social media, and imaging devices such as smart phone and wearable cameras. Experimental results show a high accuracy of 99.2% on the food/non-food classification and 83.6% on the food category recognition