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Author |
Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Bayesian deep learning for semantic segmentation of food images |
Type |
Journal Article |
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Year |
2022 |
Publication |
Computers and Electrical Engineering |
Abbreviated Journal |
CEE |
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Volume |
103 |
Issue ![sorted by Issue field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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Pages |
108380 |
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Keywords |
Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis |
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Abstract |
Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. |
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October 2022 |
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Science Direct |
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MILAB |
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no |
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Admin @ si @ ANR2022 |
Serial |
3763 |
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Author |
Bhalaji Nagarajan; Marc Bolaños; Eduardo Aguilar; Petia Radeva |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
Deep ensemble-based hard sample mining for food recognition |
Type |
Journal Article |
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Year |
2023 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
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Volume |
95 |
Issue ![sorted by Issue field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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Pages |
103905 |
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Abstract |
Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics. |
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MILAB |
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no |
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Admin @ si @ NBA2023 |
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3844 |
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Author |
Mohammad Momeny; Ali Asghar Neshat; Ahmad Jahanbakhshi; Majid Mahmoudi; Yiannis Ampatzidis; Petia Radeva |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN |
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Journal Article |
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Year |
2023 |
Publication |
Food Control |
Abbreviated Journal |
FC |
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Volume |
147 |
Issue ![sorted by Issue field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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Pages |
109554 |
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Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU. |
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MILAB |
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no |
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Admin @ si @ MNJ2023 |
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3882 |
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Author |
Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification |
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Journal Article |
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2009 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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10 |
Issue ![sorted by Issue field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
1 |
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113–126 |
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The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination. |
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1524-9050 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ BEV2008 |
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1116 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
On the Decoding Process in Ternary Error-Correcting Output Codes |
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Journal Article |
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2010 |
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IEEE on Pattern Analysis and Machine Intelligence |
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TPAMI |
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32 |
Issue ![sorted by Issue field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
1 |
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120–134 |
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A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved. |
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0162-8828 |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ EPR2010b |
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1277 |
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Permanent link to this record |