Continual Learning for Food Recognition Using Class Incremental Extreme and Online Clustering Method: Self-Organizing Incremental Neural Network
Rashida Bansiwala, Pramod B. Gosavi, Rahul Gaikwad · International Journal of Innovations in Engineering and Science · 2021
Nowadays, standard intake of healthy food is necessary for keeping a balanced diet to avoid obesity in the human body.Literature has indicated that accurate dietary assessment is very important for assessing the effectiveness of weight loss interventions.However, most of the existing dietary assessment methods rely on memory.With the help of pervasive mobile devices and rich cloud services, it is now possible to develop new computer-aided food recognition system for accurate dietary assessment.But Food Recognition does not allow data incremental learning and often suffer from catastrophic interference problems during the class incremental learning.This is an important issue in food recognition since real-world food datasets are open-ended and dynamic, involving a continuous increase in food samples and food classes.Model retraining is often carried out to cope with the dynamic nature of the data, but this demands high-end computational resources and significant time.This paper proposes a new open-ended continual learning framework by employing transfer learning on deep models for feature extraction, Relief F for feature selection, and a novel adaptive reduced class incremental kernel extreme learning machine (ARCIKELM) for classification.Relief F reduces computational complexity by ranking and selecting the extracted features.The novel ARCIKELM classifier dynamically adjusts network architecture to reduce catastrophic forgetting.It addresses domain adaptation problems when new samples of the existing class arrive.Results show that the proposed framework learns new classes incrementally with less catastrophic inference and adapts domain changes while having competitive classification performance.