Categorical Effects in the Perception of Familiar Faces

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 71-71
Author(s):  
A Angeli ◽  
W Gerbino
Keyword(s):  
Classification Accuracy ◽  
Control Experiment ◽  
Categorical Perception ◽  
Discrimination Accuracy ◽  
Sequential Classification ◽  
Better Than ◽  
The Continuum

Photographs of morphed faces were shown to close friends of portrayed individuals. Three tasks were used: localisation of a morphed target on the continuum between the two original faces, simultaneous same - different discrimination of face pairs separated by a 20% morphing step (AB task), and sequential classification of the same pairs (ABX task). Localisation data were plotted against morph coefficients. Evidence of categorical processing was provided by steeper functions for upright vs upside-down faces. In the AB task, intermediate faces were discriminated better than faces separated by the same morphing step but closer to one original. This was confirmed in a control experiment where the participants were unfamiliar with portrayed individuals and were unlikely to process our stimuli categorically. The superiority of intermediate faces in the AB task was attributed to a nonlinearity of continua generated by the morphing procedure, and used as a baseline to evaluate ABX classification data. Also in the ABX task, intermediate faces, those straddling the categorical boundary, were classified more accurately than faces located on the same side of the boundary. However, the superiority in classification accuracy was larger than the superiority in discrimination accuracy operationalised by the AB task, as predicted by the categorical perception hypothesis.

Performance Comparison of Methods for Tree Species Classification in Multispectral Images

1970 ◽  
Vol 111 (5) ◽  
pp. 119-122 ◽  
Author(s):  
R. Dinuls ◽  
A. Lorencs ◽  
I. Mednieks
Keyword(s):  
Classification Accuracy ◽  
Field Work ◽  
Performance Comparison ◽  
Multispectral Images ◽  
Species Classification ◽  
Multispectral Data ◽  
Individual Trees ◽  
Favorable Conditions ◽  
Better Than

A number of methods for classification of individual trees in high resolution multispectral images have been developed. The paper provides comparative analysis of some practicable methods of such type. Classification accuracy into 5 species was tested by computer simulations with real multispectral data obtained using airborne hyperspectral sensor. Coordinates and species of individual trees were supplied for testing by field work. It is shown that classification accuracy better than 97 % can be reached by more sophisticated methods in favorable conditions. Presented results can be used to choose a classification method appropriate for the particular forest inventory task. Ill. 1, bibl. 7 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.371

scholarly journals Research on Intelligent Recognition and Classification Algorithm of Music Emotion in Complex System of Music Performance

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Daliang Wang ◽  
Xiaowen Guo
Keyword(s):  
Complex System ◽  
Classification Accuracy ◽  
Music Performance ◽  
Classification Algorithm ◽  
Audio Signals ◽  
Fusion Algorithm ◽  
Before And After ◽  
Intelligent Recognition ◽  
Better Than

In the complex system of music performance, there are differences in the expression of music emotions by listeners, so it is of great significance to study the classification of different emotions under different audio signals. In this paper, the research of human emotional intelligence recognition and classification algorithm in the complex system of music performance is proposed. Through the recognition of SVM, KNN, ANN, and ID3 classifiers, the accuracy of a single classifier is compared, and then the four classifiers are combined to compare the classification accuracy of audio signals before and after preprocessing. The results show that the accuracy of SVM and ANN fusion is the highest. Finally, recall and F1 are comprehensively compared in the fusion algorithm, and the fusion classification effect of SVM and ANN is better than that of the algorithm model.

scholarly journals EEG-based classification of natural sounds reveals specialized responses to speech and music

2019 ◽  
Author(s):  
Nathaniel J Zuk ◽  
Emily S Teoh ◽  
Edmund C Lalor
Keyword(s):  
Classification Accuracy ◽  
Average Power ◽  
Cost Effective ◽  
List Type ◽  
Natural Sounds ◽  
Brain Responses ◽  
Species Specific ◽  
The Brain ◽  
Better Than

AbstractHumans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger and more temporally precise for human-specific sounds like speech and music, as has been found for responses to species-specific sounds in other animals. We recorded EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By classifying each sound based on the EEG response, we found that speech, music, and impact sounds were classified better than other natural sounds. But unlike impact sounds, the classification accuracy for speech and music dropped for synthesized sounds that have identical “low-level” acoustic statistics based on a subcortical model, indicating a selectivity for higher-order features in these sounds. Lastly, the trends in average power and phase consistency of the two-second EEG responses to each sound replicated the patterns of speech and music selectivity observed with classification accuracy. Together with the classification results, this suggests that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds. In addition to highlighting the importance of speech and music for the human brain, the techniques used here could be a cost-effective and efficient way to study the human brain’s selectivity for speech and music in other populations.HighlightsEEG responses are stronger to speech and music than to other natural soundsThis selectivity was not replicated using stimuli with the same acoustic statisticsThese techniques can be a cost-effective way to study speech and music selectivity

Enhancement of Classification Accuracy of a Time-frequency Approach for an EEG-based Brain-computer Interface

2007 ◽  
Vol 46 (02) ◽  
pp. 155-159 ◽  
Author(s):  
N. Yamawaki ◽  
C. Wilke ◽  
L. Hue ◽  
Z. Liu ◽  
B. He
Keyword(s):  
Classification Accuracy ◽  
Control Experiment ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Accuracy Rate ◽  
Time Frequency ◽  
Cursor Control ◽  
Band Pass ◽  
Line Analysis

Summary Objectives : The aim of this paper is to develop a new algorithm to enhance the performance of EEG-based brain-computer interface (BCI). Methods : We improved our time-frequency approach of classification of motor imagery (MI) tasks for BCI applications. The approach consists of Laplacian filtering, band-pass filtering and classification by correlation of time-frequency-spatial patterns. Results and Conclusions : Through off-line analysis of data collected during a “cursor control" experiment, we evaluated the capability of our new method to reveal major features of the EEG control for enhancement of MI classification accuracy. The pilot results in a human subject are promising, with an accuracy rate of 96.1%.

scholarly journals Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 233
Author(s):  
Dong-Woon Lee ◽  
Sung-Yong Kim ◽  
Seong-Nyum Jeong ◽  
Jae-Hong Lee
Keyword(s):  
Dental Implant ◽  
Classification Accuracy ◽  
Reliability And Validity ◽  
Mechanical Complication ◽  
Radiographic Images ◽  
Acceptable Accuracy ◽  
Reliable Detection ◽  
Classification Evaluation ◽  
Accuracy Performance

Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

Sign in / Sign up

Export Citation Format

Share Document