A supervised extreme learning committee for food recognition

Food recognition is an emerging topic in computer vision. The problem is being addressed especially in health-oriented systems where it is used as a support for food diary applications. The goal is to improve current food diaries, where the users have to manually insert their daily food intake, with an automatic recognition of the food type, quantity and consequent calories intake estimation. In addition to the classical recognition challenges, the food recognition problem is characterized by the absence of a rigid structure of the food and by large intra-class variations. To tackle such challenges, a food recognition system based on a committee classification is proposed. The aim is to provide a system capable of automatically choosing the optimal features for food recognition out of the existing plethora of available ones (e.g., color, texture, etc.). Following this idea, each committee member, i.e., an Extreme Learning Machine, is trained to specialize on a single feature type. Then, a Structural Support Vector Machine is exploited to produce the final ranking of possible matches by filtering out the irrelevant features and thus merging only the relevant ones. Experimental results show that the proposed system outperforms state-of-the-art works on four publicly available benchmark datasets. \ua9 2016 Elsevier Inc. All rights reserved

A Structured Committee for Food Recognition

Food recognition is an emerging computer vision topic. The problem is characterized by the absence of rigid structure of the food and by the large intra-class variations. Existing approaches tackle the problem by designing ad-hoc feature representations based on a priori knowledge of the problem. Differently from these, we propose a committee-based recognition system that chooses the optimal features out of the existing plethora of available ones (e.g., color, texture, etc.). Each committee member is an Extreme Learning Machine trained to classify food plates on the basis of a single feature type. Single member classifications are then considered by a structural Support Vector Machine to produce the final ranking of possible matches. This is achieved by filtering out the irrelevant features/classifiers, thus considering only the relevant ones. Experimental results show that the proposed system outperforms state-of-the-art works on the most used three publicly available benchmark datasets

Artificial Intelligence in the Context of Human Consciousness

Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware

Deep Transfer Learning for Food Recognition

Food Recognition is an essential topic in the area of computer of its target applications is to avoid achieving a cashier at the dining place. In this paper, we investigate the application of Deep Transfer Learning for food recognition. We fine-tune three well learning models namely; AlexNet, GoogleNet, and Vgg16. The fine tuning procedure depends on removing the last three layers of each model and adds another five new layers. The training and validation of each model conducted through food a dataset collected from our university's canteen. The dataset contains 39 food types, 20 images for each type. The fine-tuned models show similar training and validation performance and achieved 100% accuracy over the small-scale dataset

Five-country Study on Service and Volunteering in Southern Africa: Zambia Country Report

The study documents and analyses civic service and volunteering in Zambia and also identifies formal and informal civic service programmes in Zambia