HEp-2 Cell Classification Using Descriptors Fused into the Dissimilarity Space into the Dissimilarity Space

2014 ◽  
Vol 23 (03) ◽  
pp. 1460006
Author(s):  
Ilias Theodorakopoulos ◽  
Dimitris Kastaniotis ◽  
George Economou ◽  
Spiros Fotopoulos
Keyword(s):  
Autoimmune Diseases ◽  
Classification Scheme ◽  
State Of The Art ◽  
Svm Classifier ◽  
Cell Level ◽  
Rotation Invariant ◽  
Classification Rate ◽  
Stage Classification ◽  
Linear Svm ◽  
Serum Detection

Autoimmune diseases are strictly connected with the presence of autoantibodies in patient serum. Detection of Antinucleolar Antibodies (ANAs) in patient serum is performed using a laboratory technique named Indirect Immunofluorescence (IIF) followed by manual evaluation on the acquired slides from specialized personnel. In this procedure, several limitations appear and several automatic techniques have been proposed for the task of ANA detection. In this work we present a method achieving state-of-the-art performance on a publicly available dataset. More precisely, two powerful and rotation invariant descriptors are incorporated into a two stage classification scheme where the feature vectors are represented and fused in the dissimilarity space. Then, in a second level dissimilarity vectors are classified using a linear SVM classifier. Evaluation on the HEp-2 cell contest dataset yields a 70.16% performance on cell-level classification. Furthermore we provide results in Image Level Classification where a 78.57% classification rate was achieved.

Extended Mapping Local Binary Pattern Operator for Texture Classification

2017 ◽  
Vol 31 (06) ◽  
pp. 1750019 ◽  
Author(s):  
Mohammad Hossein Shakoor ◽  
Reza Boostani
Keyword(s):  
Local Binary Pattern ◽  
Accurate Result ◽  
State Of The Art ◽  
Texture Classification ◽  
Texture Feature ◽  
Mapping Technique ◽  
Texture Feature Extraction ◽  
Rotation Invariant ◽  
Classification Rate ◽  
Extended Mapping

In this paper, an Extended Mapping Local Binary Pattern (EMLBP) method is proposed that is used for texture feature extraction. In this method, by extending nonuniform patterns a new mapping technique is suggested that extracts more discriminative features from textures. This new mapping is tested for some LBP operators such as CLBP, LBP, and LTP to improve the classification rate of them. The proposed approach is used for coding nonuniform patterns into more than one feature. The proposed method is rotation invariant and has all the positive points of previous approaches. By concatenating and joining two or more histograms significant improvement can be made for rotation invariant texture classification. The implementation of proposed mapping on Outex, UIUC and CUReT datasets shows that proposed method can improve the rate of classifications. Furthermore, the introduced mapping can increase the performance of any rotation invariant LBP, especially for large neighborhood. The most accurate result of the proposed technique has been obtained for CLBP. It is higher than that of some state-of-the-art LBP versions such as multiresolution CLBP and CLBC, DLBP, VZ_MR8, VZ_Joint, LTP, and LBPV.

Enhancing WO3 gas sensor selectivity using a set of pollutant detection classifiers

Sensor Review ◽  
2018 ◽  
Vol 38 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Rabeb Faleh ◽  
Sami Gomri ◽  
Mehdi Othman ◽  
Khalifa Aguir ◽  
Abdennaceur Kachouri
Keyword(s):  
Hybrid Approach ◽  
Principal Component ◽  
Sensor Data ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Classification Rate ◽  
Content Type ◽  
Linear Svm ◽  
The Individual

Purpose In this paper, a novel hybrid approach aimed at solving the problem of cross-selectivity of gases in electronic nose (E-nose) using the combination classifiers of support vector machine (SVM) and k-nearest neighbors (KNN) methods was proposed. Design/methodology/approach First, three WO3 sensors E-nose system was used for data acquisition to detect three gases, namely, ozone, ethanol and acetone. Then, two transient parameters, derivate and integral, were extracted for each gas response. Next, the principal component analysis (PCA) was been applied to extract the most relevant sensor data and dimensionality reduction. The new coordinates calculated by PCA were used as inputs for classification by the SVM method. Finally, the classification achieved by the KNN method was carried out to calculate only the support vectors (SVs), not all the data. Findings This work has proved that the proposed fusion method led to the highest classification rate (100 per cent) compared to the accuracy of the individual classifiers: KNN, SVM-linear, SVM-RBF, SVM-polynomial that present, respectively, 89, 75.2, 80 and 79.9 per cent as classification rate. Originality/value The authors propose a fusion classifier approach to improve the classification rate. In this method, the extracted features are projected into the PCA subspace to reduce the dimensionality. Then, the obtained principal components are introduced to the SVM classifier and calculated SVs which will be used in the KNN method.

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

scholarly journals Machine learning identifies abnormal Ca2+ transients in human induced pluripotent stem cell-derived cardiomyocytes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hyun Hwang ◽  
Rui Liu ◽  
Joshua T. Maxwell ◽  
Jingjing Yang ◽  
Chunhui Xu
Keyword(s):  
Machine Learning ◽  
Stem Cell ◽  
Pluripotent Stem Cell ◽  
Induced Pluripotent Stem Cell ◽  
Expert Assessment ◽  
Svm Classifier ◽  
Cell Level ◽  
Human Expert ◽  
Analytical Algorithm ◽  
Induced Pluripotent

Abstract Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca2+ transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca2+ transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca2+ transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profiled peak variables as predictive features. Third, we train another cell-level SVM classifier by using human-expert assessment of cell abnormality as outcome and quantified cell-level variables as predictive features. This cell-level SVM classifier can be used to assess additional Ca2+ transient signals. By applying this pipeline to our Ca2+ transient data, we trained a cell-level SVM classifier using 200 cells as training data, then tested its accuracy in an independent dataset of 54 cells. As a result, we obtained 88% training accuracy and 87% test accuracy. Further, we provide a free R package to implement our pipeline for high-throughput CM Ca2+ analysis.

scholarly journals Analysis of the Repeatability of SIFT and SURF Descriptors Techniques for Underwater Image Preprocessing

2021 ◽  
pp. 24-31
Author(s):  
Shubhangi N. Ghate ◽  
Dr. Mangesh Nikose
Keyword(s):  
Prior Knowledge ◽  
State Of The Art ◽  
Rotation Invariant ◽  
Image Preprocessing ◽  
Retrieval Time ◽  
Retrieval Accuracy ◽  
Invariant Features ◽  
Underwater Image ◽  
Geometrical Constraints ◽  
Time Required

To improve the repeatability of SIFT and SURF descriptors, we conducted research to find two methods: first, a method for pre-processing underwater images that does not require prior knowledge of the scene, and second, a method for computing distances that is less expensive in terms of execution time for finding corresponding points. SIFTs (Scale and Rotation Invariant Features) are new features that have been developed. SIFTs (Scale and Rotation Invariant Features) are newly developed features that are based on geometrical constraints between pairs of nearby points around a key point. SIFT is contrasted with cutting-edge local features. SIFT outperforms the state-of-the-art in terms of retrieval time and retrieval accuracy. We have discussed the time required to extract key point features of SIFT and SURF Descriptor.

A New Pedestrian Feature Description Method Named Neighborhood Descriptor of Oriented Gradients

2021 ◽  
Vol 16 (1) ◽  
pp. 23-55
Author(s):  
Qian Liu ◽  
Feng Yang ◽  
XiaoFen Tang
Keyword(s):  
Feature Vector ◽  
Pedestrian Detection ◽  
Weight Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Local Weight ◽  
Detection Approach ◽  
Training Samples ◽  
New Feature ◽  
Linear Svm

In view of the issue of the mechanism for enhancing the neighbourhood relationship of blocks of HOG, this paper proposes neighborhood descriptor of oriented gradients (NDOG), an improved feature descriptor based on HOG, for pedestrian detection. To obtain the NDOG feature vector, the algorithm calculates the local weight vector of the HOG feature descriptor, while integrating spatial correlation among blocks, concatenates this weight vector to the tail of the HOG feature descriptor, and uses the gradient norm to normalize this new feature vector. With the proposed NDOG feature vector along with a linear SVM classifier, this paper develops a complete pedestrian detection approach. Experimental results for the INRIA, Caltech-USA, and ETH pedestrian datasets show that the approach achieves a lower miss rate and a higher average precision compared with HOG and other advanced methods for pedestrian detection especially in the case of insufficient training samples.

Gaits Classification of Normal vs. Patients by Wireless Gait Sensor and Support Vector Machine (SVM) Classifier

2017 ◽  
Vol 5 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Taro Nakano ◽  
B.T. Nukala ◽  
J. Tsay ◽  
Steven Zupancic ◽  
Amanda Rodriguez ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Human Subjects ◽  
Wearable Sensors ◽  
Normal Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Dynamic ◽  
Linear Svm ◽  
Dynamic Gait

Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, the authors took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, the authors used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, the authors should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.

scholarly journals Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

2003 ◽  
Vol 15 (7) ◽  
pp. 1667-1689 ◽  
Author(s):  
S. Sathiya Keerthi ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Heuristic Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Generalization Errors

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

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