Food is essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding human behavior, improving human health, and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from different sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food-related issues in medicine, biology, gastronomy, and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, a series of works has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews that shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this article, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.

Food recognition plays an important role in food choice and intake, which is essential to the health and well‐being of humans. It is thus of importance to the computer vision community, and can further support many food-oriented vision and multimodal tasks, e.g., food detection and segmentation, cross-modal recipe retrieval and generation. Unfortunately, we have witnessed remarkable advancements in generic visual recognition for released large-scale datasets, yet largely lags in the food domain. In this paper, we introduce Food2K, which is the largest food recognition dataset with 2,000 categories and over 1 million images. Compared with existing food recognition datasets, Food2K bypasses them in both categories and images by one order of magnitude, and thus establishes a new challenging benchmark to develop advanced models for food visual representation learning. Furthermore, we propose a deep progressive region enhancement network for food recognition, which mainly consists of two components, namely progressive local feature learning and region feature enhancement. The former adopts improved progressive training to learn diverse and complementary local features, while the latter utilizes self-attention to incorporate richer context with multiple scales into local features for further local feature enhancement. Extensive experiments on Food2K demonstrate the effectiveness of our proposed method. More importantly, we have verified better generalization ability of Food2K in various tasks, including food image recognition, food image retrieval, cross-modal recipe retrieval, food detection and segmentation. Food2K can be further explored to benefit more food-relevant tasks including emerging and more complex ones (e.g., nutritional understanding of food), and the trained models on Food2K can be expected as backbones to improve the performance of more food-relevant tasks. We also hope Food2K can serve as a large scale fine-grained visual recognition benchmark, and contributes to the development of large scale fine-grained visual analysis.

Recently, food recognition has received more and more attention in image processing and computer vision for its great potential applications in human health. Most of existing methods directly extracted deep visual features via Convolutional Neural Networks (CNNs) for food recognition. Such methods ignore characteristics of food images and are thus hard to achieve optimal recognition performance. In contrast to general object recognition, food images typically do not exhibit distinctive spatial arrangement and common semantic patterns. In this paper, we propose a Multi-Scale Multi-View Feature Aggregation (MSMVFA) scheme for food recognition. MSMVFA can aggregate high-level semantic features, mid-level attribute features and deep visual features into a unified representation. These three types of features describe the food image from different granularity. Therefore, the aggregated features can capture the semantics of food images with the greatest probability. For that solution, we utilize additional ingredient knowledge to obtain mid-level attribute representation via ingredient-supervised CNNs. High-level semantic features and deep visual features are extracted from class-supervised CNNs. Considering food images do not exhibit distinctive spatial layout in many cases, MSMVFA fuses multi-scale CNN activations for each type of features to make aggregated features more discriminative and invariable to geometrical deformation. Finally, the aggregated features are more robust, comprehensive and discriminative via the twolevel fusion, namely multi-scale fusion for each type of features and multi-view aggregation for different types of features. In addition, MSMVFA is general and different deep networks can be easily applied into this scheme. Extensive experiments and evaluations demonstrate that our method achieves state-of-theart recognition performance on three popular large-scale food benchmark datasets in Top-1 recognition accuracy. Furthermore, we expect this work will further the agenda of food recognition in the community of image processing and computer vision.

Automatically describing the content of an image has been attracting considerable research attention in the multimedia field. To represent the content of an image, many approaches directly utilize convolutional neural networks (CNNs) to extract visual representations, which are fed into recurrent neural networks (RNNs) to generate natural language. Recently, some approaches have detected semantic concepts from images and then encoded them into high-level representations. Although substantial progress has been achieved, most of the previous methods treat entities in images individually, thus lacking structured information that provides important cues for image captioning. In this work, we propose a framework based on scene graphs for image captioning. Scene graphs contain abundant structured information because they not only depict object entities in images but also present pairwise relationships. To leverage both visual features and semantic knowledge in structured scene graphs, we extract CNN features from the bounding box offsets of object entities for visual representations, and extract semantic relationship features from triples (e.g., man riding bike) for semantic representations . After obtaining these features, we introduce a hierarchical-attention-based module to learn discriminative features for word generation at each time step. The experimental results on benchmark datasets demonstrate the superiority of our method compared with several state-of-the-art methods.

Plant disease diagnosis is very critical for agriculture due to its importance for increasing crop production. Recent advances in image processing offer us a new way to solve this issue via visual plant disease analysis. However, there are few works in this area, not to mention systematic researches. In this paper, we systematically investigate the problem of visual plant disease recognition for plant disease diagnosis. Compared with other types of images, plant disease images generally exhibit randomly distributed lesions, diverse symptoms and complex backgrounds, and thus are hard to capture discriminative information. To facilitate the plant disease recognition research, we construct a new large-scale plant disease dataset with 271 plant disease categories and 220,592 images. Based on this dataset, we tackle plant disease recognition via reweighting both visual regions and loss to emphasize diseased parts. We first compute the weights of all the divided patches from each image based on the cluster distribution of these patches to indicate the discriminative level of each patch. Then we allocate the weight to each loss for each patch-label pair during weakly-supervised training to enable discriminative disease part learning. We finally extract patch features from the network trained with loss reweighting, and utilize the LSTM network to encode the weighed patch feature sequence into a comprehensive feature representation. Extensive evaluations on this dataset and another public dataset demonstrate the advantage of the proposed method. We expect this research will further the agenda of plant disease recognition in the community of image processing.