Proceedings ArticleDOI
A food image recognition system with Multiple Kernel Learning
Taichi Joutou,Keiji Yanai +1 more
- pp 285-288
TLDR
This paper proposes an automatic food image recognition system for recording people's eating habits and uses the Multiple Kernel Learning (MKL) method to integrate several kinds of image features such as color, texture and SIFT adaptively.Abstract:
Since health care on foods is drawing people's attention recently, a system that can record everyday meals easily is being awaited. In this paper, we propose an automatic food image recognition system for recording people's eating habits. In the proposed system, we use the Multiple Kernel Learning (MKL) method to integrate several kinds of image features such as color, texture and SIFT adaptively. MKL enables to estimate optimal weights to combine image features for each category. In addition, we implemented a prototype system to recognize food images taken by cellular-phone cameras. In the experiment, we have achieved the 61.34% classification rate for 50 kinds of foods. To the best of our knowledge, this is the first report of a food image classification system which can be applied for practical use.read more
Citations
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Book ChapterDOI
Food-101 – Mining Discriminative Components with Random Forests
TL;DR: A novel method to mine discriminative parts using Random Forests (rf), which allows us to mine for parts simultaneously for all classes and to share knowledge among them, and compares nicely to other s-o-a component-based classification methods.
Proceedings ArticleDOI
Recognition of Multiple-Food Images by Detecting Candidate Regions
TL;DR: A feature-fusion-based food recognition method for bounding boxes of the candidate regions with various kinds of visual features including bag-of-features of SIFT and CSIFT with spatial pyramid, histogram of oriented gradient (HoG), and Gabor texture features is applied.
Journal ArticleDOI
Application of Deep Learning in Food: A Review.
TL;DR: A brief introduction of deep learning was provided and the structure of some popular architectures of deep neural networks and the approaches for training a model were detailed, indicating that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection.
Journal ArticleDOI
A food recognition system for diabetic patients based on an optimized bag-of-features model
TL;DR: The proposed methodology for automatic food recognition, based on the bag-of-features (BoF) model, achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.
Journal ArticleDOI
Food Recognition: A New Dataset, Experiments, and Results
TL;DR: A new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications by designing an automatic tray analysis pipeline that takes a tray image as input, finds the regions of interest, and predicts for each region the corresponding food class.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Proceedings ArticleDOI
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Proceedings Article
Visual categorization with bags of keypoints
TL;DR: This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches and shows that it is simple, computationally efficient and intrinsically invariant.
Proceedings ArticleDOI
Automated Flower Classification over a Large Number of Classes
M.-E. Nilsback,Andrew Zisserman +1 more
TL;DR: Results show that learning the optimum kernel combination of multiple features vastly improves the performance, from 55.1% for the best single feature to 72.8% forThe combination of all features.
Journal ArticleDOI
Learning the Kernel Matrix with Semidefinite Programming
Gert R. G. Lanckriet,Nello Cristianini,Peter L. Bartlett,Laurent El Ghaoui,Michael I. Jordan +4 more
TL;DR: This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.