Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration

Min, Weiqing; Jiang, Shuqiang; Sang, Jitao; Wang, Huayang; Liu, Xinda; Herranz, Luis

doi:10.1109/tmm.2016.2639382

Cited by 91 publications

(43 citation statements)

References 38 publications

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“…[Su et al 2014] treated the ingredients as features and constructed different classifiers to predict cuisine labels of recipes. [Min et al 2017a] utilized a multimodal deep Boltzmann machine to explore both visual and ingredient information for multi-modal recipe classification. In addition, [Druck 2013] utilized various information from the recipe, including the title, a set of ingredients and an ordered list of preparation steps for recipe attribute (such as tastes and flavors) prediction.…”

Section: Recognitionmentioning

confidence: 99%

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A Survey on Food Computing

et al. 2019

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Food is very essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding the human behavior, improving the 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 disparate 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, vast amounts of work has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews, which shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this paper, 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.

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Section: Recognitionmentioning

confidence: 99%

“…[Chen et al 2017a] exploited rich food attributes for cross-modal recipe retrieval. [Min et al 2017a] utilized a multi-modal Deep Boltzmann Machine for recipe-image retrieval. [Salvador et al 2017] developed a hybrid neural Fig.…”

Section: Referencementioning

confidence: 99%

A Survey on Food Computing

et al. 2019

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“…In the past few years, labeled image datasets have played a critical role in high-level image understanding [Simonyan, 2007;Min, 2016;Zhao, 2018;Zhang, 2017;Xie, 2019;Shu, 2018;Wang, 2015;Hu, 2017;Hua, 2017;Liu, 2018;Huang, 2018;Xu, 2017]. However, the process of constructing manually labeled datasets is both time-consuming and labor-intensive [Deng, 2009].…”

Section: Introductionmentioning

confidence: 99%

Dynamically Visual Disambiguation of Keyword-based Image Search

Yao

Sun

Shen

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Due to the high cost of manual annotation, learning directly from the web has attracted broad attention. One issue that limits their performance is the problem of visual polysemy. To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation. Compared to existing methods, the primary advantage of our approach lies in that our approach can adapt to the dynamic changes in the search results. Our proposed framework consists of two major steps: we first discover and dynamically select the text queries according to the image search results, then we employ the proposed saliency-guided deep multi-instance learning network to remove outliers and learn classification models for visual disambiguation. Extensive experiments demonstrate the superiority of our proposed approach.

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“…The user comment on a particular food can be used to provide an interesting insight to understand the eating habits, cuisines and cultures of food [1]. However, the collective information can be used by the automated model to provide better-classified and segmented accuracy [2].…”

Section: Introductionmentioning

confidence: 99%

Fine Grained Food Image Segmentation through EA-DCNNs

Burkapalli*,

Patil

2019

IJITEE

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-The recognition of Indian food can be considered as a fine-grained visual recognition due to the same class photos may provide considerable amount of variability. Thus, an effective segmentation and classification method is needed to provide refined analysis. While only consideration of CNN may cause limitation through the absence of constraints such as shape and edge that causes output of segmentation to be rough on their edges. In order overcome this difficulty, a post-processing step is required; in this paper we proposed an EA based DCNNs model for effective segmentation. The EA is directly formulated with the DCNNs approach, which allows training step to get beneficial from both the approaches for spatial data relationship. The EA will help to get better-refined output after receiving the features from powerful DCNNs. The EA-DCNN training model contains convolution, rectified linear unit and pooling that is much relevant and practical to get optimize segmentation of food image. In order to evaluate the performance of our proposed model we will compare with the ground-truth data at several validation parameters

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Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration

Cited by 91 publications

References 38 publications

A Survey on Food Computing

A Survey on Food Computing

Dynamically Visual Disambiguation of Keyword-based Image Search

Fine Grained Food Image Segmentation through EA-DCNNs

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