Machine Learning framework for image classification

Loussaief, Sehla; Abdelkrim, Afef

doi:10.25046/aj030101

Cited by 19 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the selected function was of the Bayesian type with 30 interactions, which achieved an optimized accuracy level of 93%. This SVM model has already been reported as very efficient for food classification [13].…”

Section: Multivariate Analysismentioning

confidence: 85%

Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Oblitas

Ruiz

2020

The 1st International Electronic Conference on Food Science and Functional Foods

View full text Add to dashboard Cite

Terahertz time-domain spectroscopy is a useful technique for determining some physical characteristics of materials, and is based on selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify cocoa percentages in chocolates, the terahertz spectra (0.5–10 THz) of five chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed with the MATLAB 2019b application, from which the dielectric function was obtained along with the absorbance curves, and were classified by using 24 mathematical classification models, achieving differentiations of around 93% obtained by the Gaussian SVM algorithm model with a kernel scale of 0.35 and a one-against-one multiclass method. It was concluded that the combined processing and classification of images obtained from the terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to successfully classify chocolates with different percentages of cocoa.

show abstract

Section: Multivariate Analysismentioning

confidence: 85%

Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Oblitas

Ruiz

2020

The 1st International Electronic Conference on Food Science and Functional Foods

View full text Add to dashboard Cite

show abstract

“…Normalization has also been performed on the pixels of the raw images as a pre-processing technique to normalize all pixel values between the range of [0, 1] to enable fast computation. -Feature-extraction: Many machine learning algorithms can accomplish the task of image classification [22] [23] [24], however, all algorithms require proper features for conducting the classification. In this research, image color information is split into three different (RGB) channels as shown in Fig.…”

Section: Proposed Methodologymentioning

confidence: 99%

Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

Shehu

Browne

Eisenbarth

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.

show abstract

“…In front of a lot of circumstances, the algorithm learns which behavior to follow and decision to take creating a model. The machine makes the tasks automate depending on the situation [54][55][56][57][58][59][60][61][62]. There are three main types of Machine Learning [60]- [66] represented in figure 7: In supervised learning, the algorithms are based on already categorized datasets, in order to understand the criteria used for classification and reproducing them [67]- [70].…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence Approach for Target Classification: A State of The Art

Abdellaoui¹,

Daghouj²,

Fattah³

et al. 2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

The classification of static or mobile objects, from a signal or an image containing information as to their structure or their form, constitutes a constant concern of specialists in the electronic field. The remarkable progress made in past years, particularly in the development of neural networks and artificial intelligence systems, has further accentuated this trend. The fields of application and potential uses of Artificial Intelligence are increasingly diverse: understanding of natural language, visual recognition, robotics, autonomous system, Machine learning, etc. This paper is a state of the art on the classification of radar signals. It focuses on the contribution of artificial intelligence to the latter without forgetting target tracking. This by evoking the different feature extractors, classifiers and the existing identification deep learning algorithms. We detail also the process allowing carrying out this classification.

show abstract

Machine Learning framework for image classification

Cited by 19 publications

References 22 publications

Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

Artificial Intelligence Approach for Target Classification: A State of The Art

Contact Info

Product

Resources

About