Food - Recipe - Research Reads

Food Recipe Recommendation Based on Ingredients Detection Using Deep Learning

• Custom dataset consisting of 9856 images belonging to 32 different food ingredients classes
• Convolution Neural Network (CNN) model was used to identify food ingredients, and for recipe recommendations
• Open Computer Vision Library (OpenCV) [2], TensorFlow [3], NumPy [4], and Keras [5]

Food Ingredients Recognition through Multi-label Learning

Nutrition5k: A Comprehensive Nutrition Dataset

Attention networks can extract much richer descriptions from the images compared to pure convolutional networks

• Recipe Ingredients Dataset
• Food Ingredient Lists
• Food Ingredients and Recipes Dataset with Images
• Food Recommendation Systems
• Recipe Ingredients Analysis
• Predict cuisine type from recipe ingredients
• Food.com - Recipes and Reviews

Mining Discriminative Food Regions for Accurate Food Recognition

Taking inspiration from Adversarial Erasing, a strategy that progressively discovers discriminative object regions for weakly supervised semantic segmentation

The proposed architecture denoted as PAR-Net is end-to-end trainable, and highlights discriminative regions in an online fashion

On three food datasets chosen (Food-101, Vireo-172, and Sushi-50)

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

Firstly, the visual representations of food images are of fundamental importance as it significantly impacts classification performance 

Therefore, many food recognition methods employ handcrafted features such as shape, colour, texture, local

As consolidated large food image datasets, for example, UECFOOD-100, Food-101, UECFOOD-256, UNCIT-FD1200, UNCIT-FD889

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

One method of predicting the ingredients given a food image is cross-modal recipe retrieval which outputs the ingredients and the corresponding amounts of the retrieved recipe

We use a Resnet50 [10] pre-trained on UPMC [21] and replace the last layer with ingredient amount prediction.

Food Ingredients Recognition through Multi-label Learning

CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

Summary of Techniques

• Object Detection
• Multi label classification
• Sliding Window Detection
• Crop / Zoom Detection
• Handcrafted features / Edges / Contours
• Attributes (Color / Shape / Text Extraction)

Datasets

• http://123.57.42.89/FoodComputing-Dataset/ISIA-Food500.html
• https://github.com/ustc-vim/vegfru
• http://123.57.42.89/FoodProject.html
• https://github.com/monajalal/Kenyan-Food

Keep Exploring!!!