GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention

Sharma, Udit; Artacho, Bruno; Savakis, Andreas

doi:10.3390/s21227504

Cited by 16 publications

(14 citation statements)

References 44 publications

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“…In addition, a new database of food images with related recipes and nutritional information is collected in real-world hospital settings. Sharma et al [11] presented a GourmetNet for food segmentation with multi-scale feature representation. This network incorporates both spatial attention and channel attention using waterfall atrous spatial pooling module.…”

Section: Related Workmentioning

confidence: 99%

Semantic food segmentation for health monitoring

Hussain

Ortis

Polosa

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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This paper presents semantic food segmentation to detect individual food items in an image. The presented approach has been developed in the context of the FoodRec project, which aims to study and develop an automatic framework to track and monitor the dietary habits of people, during their smoke quitting protocol. The goal of food segmentation is to train a model that can look at the images of food items and infer semantic information to recognize individual food items present in an image. In this contribution, we propose a novel Convolutional Deconvolutional Pyramid Network for food segmentation to understand the semantic information of an image at a pixel level. This network employs convolution and deconvolution layers to build a feature pyramid and achieves high-level semantic feature map representation. As a consequence, the novel semantic segmentation network generates a dense and precise segmentation map of the input food image. Furthermore, the proposed method demonstrated significant improvements on a well-known public benchmark dataset.

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Section: Related Workmentioning

confidence: 99%

Semantic food segmentation for health monitoring

Hussain

Ortis

Polosa

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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“…However, their proposed method is more complex and less time-efficient compared to DeepLabV3+. Sharma et al [37] proposed a novel architecture named Gourmet-Net which incorporates both channel and spatial attention informations in an expanded multi-scale feature representation using advanced Waterfall Atrous Spatial Pooling (WASPv2) [38] module with channel and spatial attention mechanisms. GourmetNet achieves state-of-the-art performance on the UNIMIB2016 and UECFood-Pix datasets.…”

Section: Segmentation Model For Food Imagementioning

confidence: 99%

“…Achieving on these datasets a mIoU of 71.79% and 65.13% respectively. A more recent work, [39] proposed a Bayesian version of DeepLabv3+ and GourmetNet [37] to perform multi-class segmentation of foods.…”

Section: Segmentation Model For Food Imagementioning

confidence: 99%

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mid-DeepLabv3+ : A Novel Approach for Image Semantic Segmentation applied to African Food Dietary Assessment

EREP,

CHAARI,

ELE

et al. 2023

Preprint

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Recent decades have seen the development of vision-based dietary assessment systems (VBDA). These systems generally consist of three main stages: food image analysis, portion estimation and nutrient derivation. The effectiveness of the initial step is highly dependent on the use of accurate segmentation and image recognition models, and the availability of high-quality training datasets. Food image segmentation still faces various challenges, while most existing research has focused mainly on Asian and Western food images. For this reason, this study is based on food images from sub-Saharan Africa, which pose their own problems, such as inter-class similarity and dishes presentation with mixed-class food. This work focuses on the first stage of VBDA, where we introduce two notable contributions. Firstly, we propose mid-DeepLabv3+, an enhanced food image segmentation model based on DeepLabv3+ with a ResNet50 backbone. Our approach involves adding a middle layer in the decoder path and SimAM attention mechanism after each extracted backbone features layers. Secondly, we present CamerFood10, the first food images dataset specifically designed for sub-Saharan African food segmentation. It includes 10 classes of most consumed food items in Cameroon. On our dataset, mid-DeepLabv3+ outperforms existing image segmentation benchmark models with an mIoU (mean Intersection over Union) of 65.20\%, representing more than +10.74\% improvement over the DeepLabv3+ with the same backbone.

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“…GourmetNet achieved state-of-the-art performance on the UNIMIB2016 and UECFoodPix datasets, achieving an mIoU of 71.79% and 65.13% on these datasets, respectively. A more recent work, [ 39 ], proposed a Bayesian version of DeepLabv3+ and GourmetNet [ 37 ] to perform multi-class segmentation of foods.…”

Section: Related Workmentioning

confidence: 99%

mid-DeepLabv3+: A Novel Approach for Image Semantic Segmentation Applied to African Food Dietary Assessments

Baban A Erep,

Chaari

2023

Sensors

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Recent decades have witnessed the development of vision-based dietary assessment (VBDA) systems. These systems generally consist of three main stages: food image analysis, portion estimation, and nutrient derivation. The effectiveness of the initial step is highly dependent on the use of accurate segmentation and image recognition models and the availability of high-quality training datasets. Food image segmentation still faces various challenges, and most existing research focuses mainly on Asian and Western food images. For this reason, this study is based on food images from sub-Saharan Africa, which pose their own problems, such as inter-class similarity and dishes with mixed-class food. This work focuses on the first stage of VBDAs, where we introduce two notable contributions. Firstly, we propose mid-DeepLabv3+, an enhanced food image segmentation model based on DeepLabv3+ with a ResNet50 backbone. Our approach involves adding a middle layer in the decoder path and SimAM after each extracted backbone feature layer. Secondly, we present CamerFood10, the first food image dataset specifically designed for sub-Saharan African food segmentation. It includes 10 classes of the most consumed food items in Cameroon. On our dataset, mid-DeepLabv3+ outperforms benchmark convolutional neural network models for semantic image segmentation, with an mIoU (mean Intersection over Union) of 65.20%, representing a +10.74% improvement over DeepLabv3+ with the same backbone.

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GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention

Cited by 16 publications

References 44 publications

Semantic food segmentation for health monitoring

Semantic food segmentation for health monitoring

mid-DeepLabv3+ : A Novel Approach for Image Semantic Segmentation applied to African Food Dietary Assessment

mid-DeepLabv3+: A Novel Approach for Image Semantic Segmentation Applied to African Food Dietary Assessments

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