Nearest Neighbor For Histogram-based Feature Extraction

Mohamad, Fatma Susilawati; Manaf, Azizah Abdul; Chuprat, Suriayati

doi:10.1016/j.procs.2011.04.140

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…Specifically, the extensive experience of searching for highly saturated red (ripe) fruits as a gatherer may have contributed to the development of superior red saturation discrimination ability in females. For example, saturation has been suggested to be more effective in identifying a ripe fruit than hue [ 20 ]. Also, the fact that L-M cone photopigment coding genes are located on the X chromosome may have been advantageous for L-M opponent cone processes in females [ 12 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, studies of sex differences in color perception have yielded inconsistent results [ 12 , 15 – 19 ]. One possible explanation is that most of these studies focused on hue discrimination, rather than color saturation, which is more likely to be important for foraging efficiency when searching for ripe fruits and edible leaves [ 20 ]. We hypothesized that control females would show superiority in red color saturation discrimination, which would reflect remnants of an evolutionary sex-specific functional behavior.…”

Section: Introductionmentioning

confidence: 99%

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Colour vision in ADHD: Part 1 - Testing the retinal dopaminergic hypothesis

et al. 2014

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ObjectivesTo test the retinal dopaminergic hypothesis, which posits deficient blue color perception in ADHD, resulting from hypofunctioning CNS and retinal dopamine, to which blue cones are exquisitely sensitive. Also, purported sex differences in red color perception were explored.Methods30 young adults diagnosed with ADHD and 30 healthy young adults, matched on age and gender, performed a psychophysical task to measure blue and red color saturation and contrast discrimination ability. Visual function measures, such as the Visual Activities Questionnaire (VAQ) and Farnsworth-Munsell 100 hue test (FMT), were also administered.ResultsFemales with ADHD were less accurate in discriminating blue and red color saturation relative to controls but did not differ in contrast sensitivity. Female control participants were better at discriminating red saturation than males, but no sex difference was present within the ADHD group.ConclusionPoorer discrimination of red as well as blue color saturation in the female ADHD group may be partly attributable to a hypo-dopaminergic state in the retina, given that color perception (blue-yellow and red-green) is based on input from S-cones (short wavelength cone system) early in the visual pathway. The origin of female superiority in red perception may be rooted in sex-specific functional specialization in hunter-gather societies. The absence of this sexual dimorphism for red colour perception in ADHD females warrants further investigation.Electronic supplementary materialThe online version of this article (doi:10.1186/1744-9081-10-38) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Colour vision in ADHD: Part 1 - Testing the retinal dopaminergic hypothesis

et al. 2014

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“…where p(i, j) is the probability that a pixel with value i will be found adjacent to a pixel of value j [6]. Selection of histogram and texture features is based on their success rate in similar classification problems [48][49][50][51].…”

Section: Inverse Differencementioning

confidence: 99%

A Performance Comparison of Classification Algorithms for Rose Plants

Malik

Aslam

Nasr

et al. 2022

Computational Intelligence and Neuroscience

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One of the key roles of Botanists is to be able to recognize flowers. This role has become highly challenging given that the number of discovered flower types are nearing half a million. To support Botanists, Information Technology offers promising solutions. Specifically, machine learning techniques are intrinsically appealing due to being precise enough as required. To this aim, two observations on flower leaves are relevant and leverage flower identification: one, flower plants exhibit unique features in their leaves thus allow distinction of their co-located flowers; two, leaves have a much longer life than flowers thus preserve identity properties longer. This paper proposes the use of machine learning-based identification of rose types by leveraging the features from their leaves. For this purpose, the performance of Naive Bayes, Generalized Linear Model, Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine has been analyzed. This study optimizes the RF model by investigating and tuning its various parameters such as the number of trees, the depth of trees, and splitting criteria. The best results are achieved with gain ratio because it takes more distinct values to avoid the problems associated with Information Gain. Optimizing the number of trees and the depth of trees of RF yield better accuracy than other models. Extensive experiments are performed to analyze the results of ensemble algorithms by using the voting method for each instance. Results suggest that the performance of ensemble classifiers is superior to that of individual models.

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“…The K-Nearest Neighbor (K-NN) method is one of the nearest neighbor (NN) -based methods that can be used to compare histograms values [10]. The K value used here defines the number of nearest neighbors involved in determining the prediction of the class label in the test data.…”

Section: Classificationmentioning

confidence: 99%

Increased Mammogram Image Contrast Using Histogram Equalization And Gaussian In The Classification Of Breast Cancer

Liantoni

Sukmagautama

Myrtha

2020

j.inf.tech.comp.eng

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Breast cancer is one of the most common diseases among women in several countries. One of the most common methods to diagnose breast cancer is mammography. In this study, we propose a classification study to differentiate benign and malignant breast tumors based on mammogram image. The proposed system includes five major steps, i.e. preprocessing, histogram equalization, convolution, feature extraction, and classification. Image is cropped using region of interest (ROI) at preprocessing stage. In this study, we perform image contrast quality enhancement of the mammogram to view the breast cancer better. Image contrast enhancement uses histogram equalization and Gaussian filter. Gray-Level Co-Occurrence Matrix (GLCM) is used to extract the mammogram features. There are five features used i.e. entropy, correlation, contrast, homogeneity, and variance. The last step is to classify using naïve Bayes classifier (NBC) and k-nearest neighbor (KNN). Based on the hypothesis, the accuracy of NBC method is 90% and the accuracy of KKN method is 87.5%. So, the mammogram image contrast enhancement is well performed.

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Nearest Neighbor For Histogram-based Feature Extraction

Cited by 9 publications

References 6 publications

Colour vision in ADHD: Part 1 - Testing the retinal dopaminergic hypothesis

Colour vision in ADHD: Part 1 - Testing the retinal dopaminergic hypothesis

A Performance Comparison of Classification Algorithms for Rose Plants

Increased Mammogram Image Contrast Using Histogram Equalization And Gaussian In The Classification Of Breast Cancer

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