## 2 Literature Review  
The literature survey explores extensively the research results that concentrate on the image classification and calorie estimation. Consequently, a comparative performance analysis of the proposed models is conducted in five distinct categories, e.g., _real time, optimized time complexity, optimized space complexity, and the satisfactory score_. A _satisfactory score_ can be comprehended as a performance indicator for a system with accuracy above 80%. The executive summary of this assessment is documented in Table 1 which also presents the distinctive contribution of this study in comparison with the existing ones on this track. In [14], [PERSON] et al. propose an automated food image recognition system for 85 categories of foods by combining different image features, such as the Gabor features, the color histogram, the bag of features (BoF), and the gradient histogram with Multiple Kernel Learning (MKL). However, this work only focuses on the image classification, and not on calorie estimation. In [15], [PERSON] et al. present a food calorie and nutrition measurement system based on support vector machine (SVM). Their approach employs food image processing and utilizes nutritional information from the nutrition table. The system is deployed in smartphones, and it scores issues. In [16], [PERSON] and [PERSON] focus on a unique food image data set including mass and volume records for the foods. They exploit a deep learning technique (Faster R-CNN) for food identification and comprehensive calorie estimation. Their data set comprises of 2978 pictures. However, the approach does not consider the real-time characteristics for the calorie estimation. In [17], [PERSON] et al. focus on estimating the calorie count of the food using images as input. The food image is processed through several image processing techniques before being applied to the SVM. However, the author does not cover the real-time characteristic for the estimation. In [18], [PERSON] et al. discuss the latest object identification methods, such as you only look once, faster region convolutional neural network, and single-shot multibox detector. The authors, however, do not focus on the real-time food calorie estimation. In [13; 19; 20; 21], the authors employs a deep learning (DL)-based model for food calorie estimation based on various food images. Even so, these models are time-consuming and do not support real-time estimation. Other studies also explore the application of DL models for food calorie measurement. For instance, in [22], [PERSON] et al. uses a DL model for food calorie measurement with an error reduction of 20%. In [23], [PERSON] et al. deploy a novel DL model on the webpage images to predict food calorie content in real time. In [24], [PERSON] et al. utilize a similar approach by crawling the web for food images and preprocessing them to train a DL model for food calorie estimation. Similar work in progress estimates the calorie content of a meal directly from recipe images [25], but suffers from scalability and real-time performance issues. In [26], [PERSON] et al. use HolcLens to estimate the actual size of the food and associated calories with high recognition time. In [27], [PERSON] and [PERSON] develop a pipeline for calorie estimation and meal reproduction for different servings of the meal. However, the focus is on the accuracy only, leaving their method highly expensive in terms of computation and scalability. In [28], [PERSON] and [PERSON] introduce the concept of hungry networks in which they reconstructs the 3D shape of the dish and plate from a single image. This method increases the processing time as 3D images require a substantial amount of processing time. In [29], [PERSON] et al. aim to improve the accuracy of the segmentation processes and calorie calculation using a combination of Mask R-CNN and GrabCut algorithms, which requires approximately three minutes to compute. In [30], [PERSON] et al. targets the same with a hierarchical clustering-based transfer learning method for greater accuracy.