Evolutionary neural architecture search based on efficient CNN models population for image classification

Termritthikun, Chakkrit; Jamtsho, Yeshi; Muneesawang, Paisarn; Zhao, Jia; Lee, Ivan

doi:10.1007/s11042-022-14187-y

Cited by 4 publications

(1 citation statement)

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“…"Food" is one of the developing areas of interest for the CV community as well as multimedia [1], whereas image detection and identification remain a highly significant problem in the medical field as well. In the literature, a new food recording tool called "FoodLog" has been developed that supports users to record their daily meals with the aid of an image recovery technique [2]. However, it is extremely challenging to perform food image analyses.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%