News Video Summarization Combining SURF and Color Histogram Features [PDF]
Entropy, 2021 Because the data volume of news videos is increasing exponentially, a way to quickly browse a sketch of the video is important in various applications, such as news media, archives and publicity.
Buyun Liang +5 more
doaj +8 more sources
Face recognition algorithm using extended vector quantization histogram features. [PDF]
PLoS ONE, 2018 In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector ...
Yan Yan +3 more
doaj +5 more sources
An automated detection of glaucoma using histogram features [PDF]
International Journal of Ophthalmology, 2015 Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve.
Karthikeyan Sakthivel +1 more
doaj +6 more sources
Histogram Layers for Neural Engineered Features [PDF]
arXivIn the computer vision literature, many effective histogram-based features have been developed. These engineered features include local binary patterns and edge histogram descriptors among others and they have been shown to be informative features for a variety of computer vision tasks.
Kharsa, Salim Al +3 more
arxiv +6 more sources
Improving feature selection algorithms using normalised feature histograms [PDF]
Electronics Letters,47, 8, 490-491, 2011, 2012 The proposed feature selection method builds a histogram of the most stable features from random subsets of a training set and ranks the features based on a classifier based cross-validation. This approach reduces the instability of features obtained by conventional feature selection methods that occur with variation in training data and selection ...
A.K. Maan, A.P. James, Fan, Hall
arxiv +8 more sources
CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma [PDF]
Cancer Imaging, 2019 Objective To identify imaging markers that reflect the epidermal growth factor receptor (EGFR) mutation status by comparing computed tomography (CT) imaging-based histogram features between bone metastases with and without EGFR mutation in patients with ...
Tong-xu Shen +8 more
doaj +3 more sources
The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study. [PDF]
Biology (Basel), 2022 Simple Summary Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up.
Boca Petresc B +6 more
europepmc +2 more sources
The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions. [PDF]
Ann Transl Med, 2020 Background The present study analyzed whole-lesion histogram parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to explore the clinical value of IVIM histogram features in the differentiation of liver lesions.
Ai Z, Han Q, Huang Z, Wu J, Xiang Z.
europepmc +2 more sources
A Likelihood Ratio Classifier for Histogram Features [PDF]
2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2018 In a number of classification problems, the features are represented by histograms. Traditionally, histograms are compared by relatively simple distance measures such as the chi-square, the Kullback-Leibler, or the Euclidean distance. This paper proposes a likelihood ratio classifier for histogram features that is optimal in Neyman-Pearson sense. It is
Raghavendra Ramachandra +2 more
openaire +4 more sources
Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features [PDF]
, 2017 Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture.
Low, Cheng-Yaw, Teoh, Andrew Beng-Jin
core +2 more sources