Depth-restricted convolutional neural network—a model for Gujarati food image classification

Shah, Bhoomi; Bhavsar, Hetal

doi:10.1007/s00371-023-02893-z

Cited by 6 publications

(1 citation statement)

References 40 publications

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“…Shah and Bhavsar [14] introduced the depth-restricted-CNN (DRCNN) method in which the Transfer Learning (TL) technique was applied to a few frameworks, such as the Alexnet, Resnet-50, Inceptionv3, VGG16, and the VGG19 framework. This method was presented as a Batch Normalization (BN) approach that heavily enhances performance with a lower number of parameters.…”

Section: Related Workmentioning

confidence: 99%

Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification

Mahgoub,

Aldehim,

Almalki

et al. 2023

Biomimetics

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Food image classification, an interesting subdomain of Computer Vision (CV) technology, focuses on the automatic classification of food items represented through images. This technology has gained immense attention in recent years thanks to its widespread applications spanning dietary monitoring and nutrition studies to restaurant recommendation systems. By leveraging the developments in Deep-Learning (DL) techniques, especially the Convolutional Neural Network (CNN), food image classification has been developed as an effective process for interacting with and understanding the nuances of the culinary world. The deep CNN-based automated food image classification method is a technology that utilizes DL approaches, particularly CNNs, for the automatic categorization and classification of the images of distinct kinds of foods. The current research article develops a Bio-Inspired Spotted Hyena Optimizer with a Deep Convolutional Neural Network-based Automated Food Image Classification (SHODCNN-FIC) approach. The main objective of the SHODCNN-FIC method is to recognize and classify food images into distinct types. The presented SHODCNN-FIC technique exploits the DL model with a hyperparameter tuning approach for the classification of food images. To accomplish this objective, the SHODCNN-FIC method exploits the DCNN-based Xception model to derive the feature vectors. Furthermore, the SHODCNN-FIC technique uses the SHO algorithm for optimal hyperparameter selection of the Xception model. The SHODCNN-FIC technique uses the Extreme Learning Machine (ELM) model for the detection and classification of food images. A detailed set of experiments was conducted to demonstrate the better food image classification performance of the proposed SHODCNN-FIC technique. The wide range of simulation outcomes confirmed the superior performance of the SHODCNN-FIC method over other DL models.

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Section: Related Workmentioning

confidence: 99%

Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification

Mahgoub,

Aldehim,

Almalki

et al. 2023

Biomimetics

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Annotate and retrieve in vivo images using hybrid self-organizing map

Kaur,

Malhi,

Pannu

2023

Vis Comput

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Multimodal retrieval has gained much attention lately due to its effectiveness over uni-modal retrieval. For instance, visual features often under-constrain the description of an image in content-based retrieval; however, another modality, such as collateral text, can be introduced to abridge the semantic gap and make the retrieval process more efficient. This article proposes the application of cross-modal fusion and retrieval on real in vivo gastrointestinal images and linguistic cues, as the visual features alone are insufficient for image description and to assist gastroenterologists. So, a cross-modal information retrieval approach has been proposed to retrieve related images given text and vice versa while handling the heterogeneity gap issue among the modalities. The technique comprises two stages: (1) individual modality feature learning; and (2) fusion of two trained networks. In the first stage, two self-organizing maps (SOMs) are trained separately using images and texts, which are clustered in the respective SOMs based on their similarity. In the second (fusion) stage, the trained SOMs are integrated using an associative network to enable cross-modal retrieval. The underlying learning techniques of the associative network include Hebbian learning and Oja learning (Improved Hebbian learning). The introduced framework can annotate images with keywords and illustrate keywords with images, and it can also be extended to incorporate more diverse modalities. Extensive experimentation has been performed on real gastrointestinal images obtained from a known gastroenterologist that have collateral keywords with each image. The obtained results proved the efficacy of the algorithm and its significance in aiding gastroenterologists in quick and pertinent decision making.

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Mixture autoregressive and spectral attention network for multispectral image compression based on variational autoencoder

Kong,

Ren,

et al. 2023

Vis Comput

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Depth-restricted convolutional neural network—a model for Gujarati food image classification

Cited by 6 publications

References 40 publications

Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification

Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification

Annotate and retrieve in vivo images using hybrid self-organizing map

Mixture autoregressive and spectral attention network for multispectral image compression based on variational autoencoder

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