scholarly journals Extremal Region Selection for MSER Detection in Food Recognition

2021 ◽  
Vol 15 ◽  
pp. 1-11
Author(s):  
MOHD NORHISHAM RAZALI
Keyword(s):  
Visual Analysis ◽  
Performance Metrics ◽  
Classification Performance ◽  
Recognition Algorithm ◽  
Valuable Insight ◽  
Interest Points ◽  
Classification Rate ◽  
Region Selection ◽  
Maximally Stable Extremal Region ◽  
Food Recognition

The visual analysis of foods on social media by using food recognition algorithm provides valuable insight from the health, cultural and marketing. Food recognition offers a means to automatically recognise foods as well the useful information such as calories and nutritional estimation by using image processing and machine learning technique. The interest points in food image can be detected effectively by using Maximally Stable Extremal Region (MSER). As MSER used global segmentation and many food images have a complex background, there are numerous irrelevant interest points are detected. These interest points are considered as noises that lead to computation burden in the overall recognition process. Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. The performance of ERS algorithm is evaluated based on the classification performance metrics by using classification rate (CR), error rate (ERT), precision (Prec.) and recall (rec.) as well as the number of extremal regions produced by ERS. UECFOOD-100 and UNICT-FD1200 are the two food datasets used to benchmark the proposed algorithm. The results of this research have found that the ERS algorithm by using optimum parameters and thresholds, be able to reduce the number of extremal regions with sustained classification performance.

scholarly journals SEMANTIC SEGMENTATION OF BENTHIC COMMUNITIES FROM ORTHO-MOSAIC MAPS

2019 ◽  
Vol XLII-2/W10 ◽  
pp. 151-158 ◽  
Author(s):  
G. Pavoni ◽  
M. Corsini ◽  
M. Callieri ◽  
M. Palma ◽  
R. Scopigno
Keyword(s):  
Visual Analysis ◽  
Marine Organism ◽  
Benthic Communities ◽  
Semantic Segmentation ◽  
Classification Performance ◽  
Training Dataset ◽  
Non Invasive ◽  
Visual Sampling ◽  
Organism Identification

<p><strong>Abstract.</strong> Visual sampling techniques represent a valuable resource for a rapid, non-invasive data acquisition for underwater monitoring purposes. Long-term monitoring projects usually requires the collection of large quantities of data, and the visual analysis of a human expert operator remains, in this context, a very time consuming task. It has been estimated that only the 1-2% of the acquired images are later analyzed by scientists (Beijbom et al., 2012). Strategies for the automatic recognition of benthic communities are required to effectively exploit all the information contained in visual data. Supervised learning methods, the most promising classification techniques in this field, are commonly affected by two recurring issues: the wide diversity of marine organism, and the small amount of labeled data.</p><p> In this work, we discuss the advantages offered by the use of annotated high resolution ortho-mosaics of seabed to classify and segment the investigated specimens, and we suggest several strategies to obtain a considerable per-pixel classification performance although the use of a reduced training dataset composed by a single ortho-mosaic. The proposed methodology can be applied to a large number of different species, making the procedure of marine organism identification an highly adaptable task.</p>

scholarly journals Efficient pan-cancer whole-slide image classification and outlier detection using convolutional neural networks

2019 ◽  
Author(s):  
Seda Bilaloglu ◽  
Joyce Wu ◽  
Eduardo Fierro ◽  
Raul Delgado Sanchez ◽  
Paolo Santiago Ocampo ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Classification Problem ◽  
Classification Performance ◽  
Neoplastic Tissue ◽  
Multiple Tumor ◽  
Slide Image ◽  
Prediction Systems ◽  
Multi Class Classification ◽  
The Many ◽  
Whole Slide Images

AbstractVisual analysis of solid tissue mounted on glass slides is currently the primary method used by pathologists for determining the stage, type and subtypes of cancer. Although whole slide images are usually large (10s to 100s thousands pixels wide), an exhaustive though time-consuming assessment is necessary to reduce the risk of misdiagnosis. In an effort to address the many diagnostic challenges faced by trained experts, recent research has been focused on developing automatic prediction systems for this multi-class classification problem. Typically, complex convolutional neural network (CNN) architectures, such as Google’s Inception, are used to tackle this problem. Here, we introduce a greatly simplified CNN architecture, PathCNN, which allows for more efficient use of computational resources and better classification performance. Using this improved architecture, we trained simultaneously on whole-slide images from multiple tumor sites and corresponding non-neoplastic tissue. Dimensionality reduction analysis of the weights of the last layer of the network capture groups of images that faithfully represent the different types of cancer, highlighting at the same time differences in staining and capturing outliers, artifacts and misclassification errors. Our code is available online at: https://github.com/sedab/PathCNN.

Automatic Paroxysmal Atrial Fibrillation Based on not Fibrillating ECGs

2004 ◽  
Vol 43 (01) ◽  
pp. 94-98 ◽  
Author(s):  
S. Mota ◽  
F. J. Toro ◽  
A. F. Díaz ◽  
F. J. Fernández ◽  
E. Ros
Keyword(s):  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
Classification Performance ◽  
Classification Algorithm ◽  
Parametric Approach ◽  
Classification Rate ◽  
Discrimination Power ◽  
Atrial Fibrillation Episode ◽  
Scanning Strategies ◽  
Automatic Algorithm

Summary Objectives: The objective of the paper is to describe an automatic algorithm for Paroxysmal Atrial Fibrillation (PAF) Detection, based on parameters extracted from ECG traces with no atrial fibrillation episode. The modular automatic classification algorithm for PAF diagnosis is developed and evaluated with different parameter configurations. Methods: The database used in this study was provided by Physiobank for The Computers in Cardiology Challenge 2001. Each ECG file in this database was translated into a 48 parameter vector. The modular classification algorithm used for PAF diagnosis was based on the nearest K-neighbours. Several configuration options were evaluated to optimize the classification performance. Results: Different configurations of the proposed modular classification algorithm were tested. The uni-parametric approach achieved a top classification rate value of 76%. A multi-parametric approach was configured using the 5 parameters with highest discrimination power, and a top classification rate of 80% was achieved; different functions to typify the parameters were tested. Finally, two automatic parametric scanning strategies, Forward and Backward methods, were adopted. The results obtained with these approaches achieved a top classification rate of 92%. Conclusions: A modular classification algorithm based on the nearest K-neighbours was designed. The classification performance of the algorithm was evaluated using different parameter configurations, typification functions and number of K-neighbors. The automatic parametric scanning techniques achieved much better results than previously tested configurations.

scholarly journals The impact of class imbalance in classification performance metrics based on the binary confusion matrix

2019 ◽  
Vol 91 ◽  
pp. 216-231 ◽  
Author(s):  
Amalia Luque ◽  
Alejandro Carrasco ◽  
Alejandro Martín ◽  
Ana de las Heras
Keyword(s):  
Performance Metrics ◽  
Confusion Matrix ◽  
Class Imbalance ◽  
Classification Performance ◽  
The Impact

scholarly journals Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

2020 ◽  
Vol 77 (4) ◽  
pp. 1545-1558
Author(s):  
Michael F. Bergeron ◽  
Sara Landset ◽  
Xianbo Zhou ◽  
Tao Ding ◽  
Taghi M. Khoshgoftaar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Performance Metrics ◽  
Early Stage ◽  
Characteristic Curve ◽  
Classification Performance ◽  
Cognitive Screening ◽  
Cross Sectional ◽  
Machine Learning Classification

Background: The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. Objective: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). Methods: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. Results: Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). Conclusion: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.

scholarly journals Surrogate regret bounds for generalized classification performance metrics

2016 ◽  
Vol 106 (4) ◽  
pp. 549-572 ◽  
Author(s):  
Wojciech Kotłowski ◽  
Krzysztof Dembczyński
Keyword(s):  
Performance Metrics ◽  
Classification Performance ◽  
Regret Bounds

MULTICLASS CLASSIFICATION BASED ON META PROBABILITY CODES

2011 ◽  
Vol 25 (08) ◽  
pp. 1219-1241 ◽  
Author(s):  
NACER FARAJZADEH ◽  
GANG PAN ◽  
ZHAOHUI WU ◽  
MIN YAO
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Feature Space ◽  
Multiclass Classification ◽  
Classification Performance ◽  
Classification Rate ◽  
New Approach ◽  
Multiclass Classifier ◽  
Original Feature

This paper proposes a new approach to improve multiclass classification performance by employing Stacked Generalization structure and One-Against-One decomposition strategy. The proposed approach encodes the outputs of all pairwise classifiers by implicitly embedding two-class discriminative information in a probabilistic manner. The encoded outputs, called Meta Probability Codes (MPCs), are interpreted as the projections of the original features. It is observed that MPC, compared to the original features, has more appropriate features for clustering. Based on MPC, we introduce a cluster-based multiclass classification algorithm, called MPC-Clustering. The MPC-Clustering algorithm uses the proposed approach to project an original feature space to MPC, and then it employs a clustering scheme to cluster MPCs. Subsequently, it trains individual multiclass classifiers on the produced clusters to complete the procedure of multiclass classifier induction. The performance of the proposed algorithm is extensively evaluated on 20 datasets from the UCI machine learning database repository. The results imply that MPC-Clustering is quite efficient with an improvement of 2.4% overall classification rate compared to the state-of-the-art multiclass classifiers.

scholarly journals Performances of proposed normalization algorithm for iris recognition

2020 ◽  
Vol 6 (2) ◽  
pp. 161 ◽  
Author(s):  
Yessi Jusman ◽  
Siew Cheok Ng ◽  
Khairunnisa Hasikin
Keyword(s):  
Iris Recognition ◽  
Correlation Energy ◽  
Texture Feature ◽  
Region Of Interest ◽  
Classification Performance ◽  
Recognition Algorithm ◽  
Small Constant ◽  
Hough Transforms ◽  
Effective System ◽  
High Recognition Accuracy

Iris recognition has very high recognition accuracy in comparison with many other biometric features. The iris pattern is not the same even right and left eye of the same person. It is different and unique. This paper proposes an algorithm to recognize people based on iris images. The algorithm consists of three stages. In the first stage, the segmentation process is using circular Hough transforms to find the region of interest (ROI) of given eye images. After that, a proposed normalization algorithm is to generate the polar images than to enhance the polar images using a modified Daugman’s Rubber sheet model. The last step of the proposed algorithm is to divide the enhance the polar image to be 16 divisions of the iris region. The normalized image is 16 small constant dimensions. The Gray-Level Co-occurrence Matrices (GLCM) technique calculates and extracts the normalized image’s texture feature. Here, the features extracted are contrast, correlation, energy, and homogeneity of the iris. In the last stage, a classification technique, discriminant analysis (DA), is employed for analysis of the proposed normalization algorithm. We have compared the proposed normalization algorithm to the other nine normalization algorithms. The DA technique produces an excellent classification performance with 100% accuracy. We also compare our results with previous results and find out that the proposed iris recognition algorithm is an effective system to detect and recognize person digitally, thus it can be used for security in the building, airports, and other automation in many applications.

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