scholarly journals Crime Scene Shoeprint Retrieval Using Hybrid Features and Neighboring Images

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 45 ◽  
Author(s):  
Yanjun Wu ◽  
Xinnian Wang ◽  
Tao Zhang
Keyword(s):  
Crime Scene ◽  
Large Set ◽  
Competitive Performance ◽  
Hybrid Features ◽  
Retrieval Method ◽  
Low Level ◽  
Image Dataset ◽  
Match Score

Given a query shoeprint image, shoeprint retrieval aims to retrieve the most similar shoeprints available from a large set of shoeprint images. Most of the existing approaches focus on designing single low-level features to highlight the most similar aspects of shoeprints, but their retrieval precision may vary dramatically with the quality and the content of the images. Therefore, in this paper, we proposed a shoeprint retrieval method to enhance the retrieval precision from two perspectives: (i) integrate the strengths of three kinds of low-level features to yield more satisfactory retrieval results; and (ii) enhance the traditional distance-based similarity by leveraging the information embedded in the neighboring shoeprints. The experiments were conducted on a crime scene shoeprint image dataset, that is, the MUES-SR10KS2S dataset. The proposed method achieved a competitive performance, and the cumulative match score for the proposed method exceeded 92.5% in the top 2% of the dataset, which was composed of 10,096 crime scene shoeprints.

scholarly journals An image dataset related to automated macrophage detection in immunostained lymphoma tissue samples

GigaScience ◽  
2020 ◽  
Vol 9 (3) ◽  
Author(s):  
Marcus Wagner ◽  
Sarah Reinke ◽  
René Hänsel ◽  
Wolfram Klapper ◽  
Ulf-Dietrich Braumann
Keyword(s):  
Reference Method ◽  
Image Data ◽  
B Cell Lymphoma ◽  
Automated Image Analysis ◽  
Large Set ◽  
Cell Nuclei ◽  
Tissue Samples ◽  
Automated Recognition ◽  
Tissue Sections ◽  
Image Dataset

Abstract Background We present an image dataset related to automated segmentation and counting of macrophages in diffuse large B-cell lymphoma (DLBCL) tissue sections. For the classification of DLBCL subtypes, as well as for providing a prognosis of the clinical outcome, the analysis of the tumor microenvironment and, particularly, of the different types and functions of tumor-associated macrophages is indispensable. Until now, however, most information about macrophages has been obtained either in a completely indirect way by gene expression profiling or by manual counts in immunohistochemically (IHC) fluorescence-stained tissue samples while automated recognition of single IHC stained macrophages remains a difficult task. In an accompanying publication, a reliable approach to this problem has been established, and a large set of related images has been generated and analyzed. Results Provided image data comprise (i) fluorescence microscopy images of 44 multiple immunohistostained DLBCL tumor subregions, captured at 4 channels corresponding to CD14, CD163, Pax5, and DAPI; (ii) ”cartoon-like” total variation–filtered versions of these images, generated by Rudin-Osher-Fatemi denoising; (iii) an automatically generated mask of the evaluation subregion, based on information from the DAPI channel; and (iv) automatically generated segmentation masks for macrophages (using information from CD14 and CD163 channels), B-cells (using information from Pax5 channel), and all cell nuclei (using information from DAPI channel). Conclusions A large set of IHC stained DLBCL specimens is provided together with segmentation masks for different cell populations generated by a reference method for automated image analysis, thus featuring considerable reuse potential.

scholarly journals Accurate Diagnosis of Diabetic Retinopathy and Glaucoma Using Retinal Fundus Images Based on Hybrid Features and Genetic Algorithm

2021 ◽  
Vol 11 (13) ◽  
pp. 6178
Author(s):  
Nasser Tamim ◽  
Mohamed Elshrkawey ◽  
Hamed Nassar
Keyword(s):  
Genetic Algorithm ◽  
Diabetic Retinopathy ◽  
Order Statistics ◽  
Large Set ◽  
Fundus Images ◽  
Hybrid Features ◽  
Short Supply ◽  
Linear Discriminant ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) and glaucoma can both be incurable if they are not detected early enough. Therefore, ophthalmologists worldwide are striving to detect them by personally screening retinal fundus images. However, this procedure is not only tedious, subjective, and labor-intensive, but also error-prone. Worse yet, it may not even be attainable in some countries where ophthalmologists are in short supply. A practical solution to this complicated problem is a computer-aided diagnosis (CAD) system—the objective of this work. We propose an accurate system to detect at once any of the two diseases from retinal fundus images. The accuracy stems from two factors. First, we calculate a large set of hybrid features belonging to three groups: first-order statistics (FOS), higher-order statistics (HOS), and histogram of oriented gradient (HOG). Then, these features are skillfully reduced using a genetic algorithm scheme that selects only the most relevant and significant of them. Finally, the selected features are fed to a classifier to detect one of three classes: DR, glaucoma, or normal. Four classifiers are tested for this job: decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), and linear discriminant analysis (LDA). The experimental work, conducted on three publicly available datasets, two of them merged into one, shows impressive performance in terms of four standard classification metrics, each computed using k-fold crossvalidation for added credibility. The highest accuracy has been provided by DT—96.67% for DR, 100% for glaucoma, and 96.67% for normal.

scholarly journals Score-based likelihood ratios for camera device identification

2020 ◽  
Vol 2020 (4) ◽  
pp. 215-1-215-8
Author(s):  
Stephanie Reinders ◽  
Li Lin ◽  
Wenhao Chen ◽  
Yong Guan ◽  
Jennifer Newman
Keyword(s):  
Identification Problem ◽  
Crime Scene ◽  
Similarity Score ◽  
Likelihood Ratios ◽  
Device Identification ◽  
Universal Detector ◽  
Image Dataset ◽  
Detector Method ◽  
Photo Response

Many areas of forensics are moving away from the notion of classifying evidence simply as a match or non-match. Instead, some use score-based likelihood ratios (SLR) to quantify the similarity between two pieces of evidence, such as a fingerprint obtained from a crime scene and a fingerprint obtained from a suspect. We apply trace-anchored score-based likelihood ratios to the camera device identification problem. We use photo-response non-uniformity (PRNU) as a camera fingerprint and one minus the normalized correlation as a similarity score. We calculate trace-anchored SLRs for 10,000 images from seven camera devices from the BOSSbase image dataset. We include a comparison between our results the universal detector method.

scholarly journals SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

2021 ◽  
Author(s):  
Abbas Cheddad ◽  
Hüseyin Kusetogullari ◽  
Agrin Hilmkil ◽  
Lena Sundin ◽  
Amir Yavariabdi ◽  
...  
Keyword(s):  
Document Analysis ◽  
Birth Date ◽  
Large Set ◽  
Learning Models ◽  
Word Spotting ◽  
The Public ◽  
Image Dataset ◽  
Comprehensive Survey ◽  
Job Title ◽  
Birth Records

AbstractThis paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms’ performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child’s first name, birth date, date of baptism, father's first/last name, mother’s first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.

A manifold ranking based method using hybrid features for crime scene shoeprint retrieval

2016 ◽  
Vol 76 (20) ◽  
pp. 21629-21649 ◽  
Author(s):  
Xinnian Wang ◽  
Chi Zhang ◽  
Yanjun Wu ◽  
Yingying Shu
Keyword(s):  
Crime Scene ◽  
Manifold Ranking ◽  
Hybrid Features

scholarly journals Neural network and random forest models in protein function prediction

2019 ◽  
Author(s):  
Kai Hakala ◽  
Suwisa Kaewphan ◽  
Jari Björne ◽  
Farrokh Mehryary ◽  
Hans Moen ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Protein Sequences ◽  
Function Prediction ◽  
Learning System ◽  
Large Set ◽  
Competitive Performance

AbstractOver the past decade, the demand for automated protein function prediction has increased due to the volume of newly sequenced proteins. In this paper, we address the function prediction task by developing an ensemble system automatically assigning Gene Ontology (GO) terms to the given input protein sequence.We develop an ensemble system which combines the GO predictions made by random forest (RF) and neural network (NN) classifiers. Both RF and NN models rely on features derived from BLAST sequence alignments, taxonomy and protein signature analysis tools. In addition, we report on experiments with a NN model that directly analyzes the amino acid sequence as its sole input, using a convolutional layer. The Swiss-Prot database is used as the training and evaluation data.In the CAFA3 evaluation, which relies on experimental verification of the functional predictions, our submitted ensemble model demonstrates competitive performance ranking among top-10 best-performing systems out of over 100 submitted systems. In this paper, we evaluate and further improve the CAFA3-submitted system. Our machine learning models together with the data pre-processing and feature generation tools are publicly available as an open source software athttps://github.com/TurkuNLP/CAFA3Author summaryUnderstanding the role and function of proteins in biological processes is fundamental for new biological discoveries. Whereas modern sequencing methods have led to a rapid growth of protein databases, the function of these sequences is often unknown and expensive to determine experimentally. This has spurred a lot of interest in predictive modelling of protein functions.We develop a machine learning system for annotating protein sequences with functional definitions selected from a vast set of predefined functions. The approach is based on a combination of neural network and random forest classifiers with features covering structural and taxonomic properties and sequence similarity. The system is thoroughly evaluated on a large set of manually curated functional annotations and shows competitive performance in comparison to other suggested approaches. We also analyze the predictions for different functional annotation and taxonomy categories and measure the importance of different features for the task. This analysis reveals that the system is particularly efficient for bacterial protein sequences.

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