Progressive kernel extreme learning machine for food image analysis via optimal features / Ghalib Ahmed Tahir - Ghalib Ahmed , Tahir (2022)
Food recognition systems recently garnered much research attention in the relevant field due to their ability to obtain objective measurements for dietary intake. The goal is to improve food diaries by addressing challenges faced by existing methodologies. In addition to the classical challenge of the absence of rigid food structure and intra-class variations, food diaries employing deep networks trained with pristine samples are susceptible to quality variations during image acquisition and transmission. Similarly, most deep learning models and other hybrid frameworks using visual features from the convolutional neural network (CNN) do not progressively learn new food categories and their ingredients. Finally, many existing frameworks integrated with dietary assessment apps are non-comprehensive, as they can not recognize food ingredients or filter non-food images from the users. This thesis tackled these challenges, aiming to provide food image analysis frameworks that use computational resources on edge devices (offline accessibility) and cloud servers (online accessibility). A framework with offline accessibility performs food image analysis on edge devices by employing efficient neural networks and a novel online data augmentation strategy random iterative mixup (RIMixUp). RIMixUp generates synthetic images during fine-tuning to train ensembles models, resilient to various quality distortions in test images. Then to increase the trust of the involved parties, this thesis proposed a user-centered explainable artificial intelligence (AI) framework by inferencing and rationalizing the results according to needs and user profile. The framework with online accessibility extracts and selects the optimal subset of quality resilient features from CNNs and subsequently incorporates the parallel type of classification. The first progressive classifier recognizes food categories, and its multilabel extension detects food ingredients. Following this idea, after extracting quality resilient features from category CNN and ingredient CNN model by fine-tuning it on synthetic images generated using the novel online data augmentation method random iterative mixup, the feature selection strategy uses SHAP scores from gradient explainer to select the reliable features. Then novel progressive kernel extreme learning machine (PKELM) is exploited to tackle domain variations due to quality distortions, intra-class variations, etc., by remodeling the network structure based on activity value with the nodes. PKELM extension for multilabel classification detects ingredients by employing bipolar step function to process test output and then selecting the column labels of the resulting matrix with value one. Moreover, during online learning, PKELM is equipped with a mechanism to label unlabeled instances and detect noisy samples. Experimental results showed superior performance of the frameworks on an integrated set of measures over other methodologies on publically available food datasets and a newly introduced dataset of Malaysian foods.