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Night-time pedestrian classification with histograms of oriented gradients-local binary patterns vectors

IET INTELLIGENT TRANSPORT SYSTEMS 2015年第1期9卷 75-85页

作者： Hurney, Patrick Waldron, Peter Morgan, Fearghal Jones, Edward Glavin, Martin Natl Univ Ireland Galway Sch Elect & Elect Engn Coll Engn & Informat Connaught Automot Res Grp Galway City Ireland Intel Shannon Shannon Clare Ireland Natl Univ Ireland Galway Sch Elect & Elect Engn Coll Engn & Informat Galway City Ireland

The use of night vision systems in vehicles is becoming increasingly common, not just in luxury cars but also in the more cost sensitive sectors. Numerous approaches using infrared sensors have been proposed in the literature to detect and classify pedestrians in low visibility situations. However, the performance of these systems is limited by the capability of the classifier. This paper presents a novel method of classifying pedestrians in far-infrared automotive imagery. Regions of interest are segmented from the infrared frame using seeded region growing. A novel method of filtering the region growing results based on the location and size of the bounding box within the frame is described. This results in a smaller number of regions of interest for classification, leading to a reduced false positive rate. Histograms of oriented gradient features and local binary pattern features are extracted from the regions of interest and concatenated to form a feature for classification. Pedestrians are tracked with a Kalman filter to increase detection rates and system robustness. Detection rates of 98%, and false positive rates of 1% have been achieved on a database of 2000 images and streams of video;this is a 3% improvement on previously reported detection rates.

关键词： pedestrians traffic engineering computing feature extraction image classification infrared detectors automobiles support vector machines image segmentation filtering theory night-time pedestrian classification histogram of oriented gradient-local binary pattern vectors night vision systems high end luxury cars low-cost infrared sensors far infrared automotive image streams support vector machine classifier region of interest RoF captured infrared frame seeded region growing filtering method bounding box ROI reduced false positive rate local binary pattern feature extraction histogram of oriented gradient feature extraction Kalman filter detection rates

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Semi-supervised protein subcellular localization

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BMC BIOINFORMATICS 2009年第1-Sup期10卷 1-10页

作者： Xu, Qian Hu, Derek Hao Xue, Hong Yu, Weichuan Yang, Qiang Hong Kong Univ Sci & Technol Bioengn Program Kowloon Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Biochem Kowloon Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China

Background: Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. Results: In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. Conclusion: Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.

关键词： support vector machine Prediction Accuracy Random Forest support vector machine classifier Label Data

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Age interval and gender prediction using PARAFAC2 and SVMs based on visual and aural features

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IET BIOMETRICS 2017年第4期6卷 290-298页

作者： Pantraki, Evangelia Kotropoulos, Constantine Lanitis, Andreas Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece Cyprus Univ Technol Dept Multimedia & Graph Arts Limassol Cyprus

Parallel factor analysis 2 (PARAFAC2) is employed to reduce the dimensions of visual and aural features and provide ranking vectors. Subsequently, score level fusion is performed by applying a support vector machine (SVM) classifier to the ranking vectors derived by PARAFAC2 to make gender and age interval predictions. The aforementioned procedure is applied to the Trinity College Dublin Speaker Ageing database, which is supplemented with face images of the speakers and two single-modality benchmark datasets. Experimental results demonstrate the advantage of using combined aural and visual features for both prediction tasks.

关键词： support vector machines feature extraction parallel processing vectors image classification speaker recognition face recognition image fusion speaker face images single-modality benchmark datasets Trinity College Dublin Speaker Ageing database SVM classifier support vector machine classifier score level fusion ranking vectors parallel factor analysis 2 aural features visual features SVM PARAFAC2 gender prediction age interval prediction

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Exhaustive prediction of disease susceptibility to coding base changes in the human genome

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BMC BIOINFORMATICS 2008年第Sup9期9卷 S3-S3页

作者： Kulkarni, Vinayak Errami, Mounir Barber, Robert Garner, Harold R. Univ Texas SW Med Ctr Dallas Mc Dermott Ctr Human Growth & Dev Dallas TX 75390 USA UT SW Med Ctr Dept Translat Res Dallas TX USA UT SW Med Ctr Dept Surg Dallas TX USA

Background: Single Nucleotide Polymorphisms (SNPs) are the most abundant form of genomic variation and can cause phenotypic differences between individuals, including diseases. Bases are subject to various levels of selection pressure, reflected in their inter-species conservation. Results: We propose a method that is not dependant on transcription information to score each coding base in the human genome reflecting the disease probability associated with its mutation. Twelve factors likely to be associated with disease alleles were chosen as the input for a support vector machine prediction algorithm. The analysis yielded 83% sensitivity and 84% specificity in segregating disease like alleles as found in the Human Gene Mutation Database from non-disease like alleles as found in the Database of Single Nucleotide Polymorphisms. This algorithm was subsequently applied to each base within all known human genes, exhaustively confirming that interspecies conservation is the strongest factor for disease association. For each gene, the length normalized average disease potential score was calculated. Out of the 30 genes with the highest scores, 21 are directly associated with a disease. In contrast, out of the 30 genes with the lowest scores, only one is associated with a disease as found in published literature. The results strongly suggest that the highest scoring genes are enriched for those that might contribute to disease, if mutated. Conclusion: This method provides valuable information to researchers to identify sensitive positions in genes that have a high disease probability, enabling them to optimize experimental designs and interpret data emerging from genetic and epidemiological studies.

关键词： support vector machine support vector machine classifier Conservation Score Synonymous Mutation Human Gene Mutation Database

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Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information

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BMC BIOINFORMATICS 2009年第s1期10卷 S34-S34页

作者： Joung, Je-Gun Fei, Zhangjun Cornell Univ Boyce Thompson Inst Plant Res Ithaca NY 14853 USA USDA Robert W Holley Ctr Agr & Hlth Ithaca NY 14853 USA

Background: MicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. Since the changes of miRNAs affect the expression of target genes, the expression levels of target genes in specific biological processes could be different from those of non-target genes. Here we demonstrate that gene expression profiles contain useful information in separating miRNA targets from non-targets. Results: The gene expression profiles related to various developmental processes and stresses, as well as the sequences of miRNAs and mRNAs in Arabidopsis, were used to determine whether a given gene is a miRNA target. It is based on the model combining the support vector machine (SVM) classifier and the scoring method based on complementary base pairing between miRNAs and mRNAs. The proposed model yielded low false positive rate and retrieved condition-specific candidate targets through a genome-wide screening. Conclusion: Our approach provides a novel framework into screening target genes by considering the gene regulation of miRNAs. It can be broadly applied to identify condition-specific targets computationally by embedding information of gene expression profiles.

关键词： support vector machine Gene Expression Profile miRNA Target support vector machine classifier Gene Expression Dataset

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A spatiotemporal risk prediction of wildlife-vehicle collisions using machine learning for dynamic warnings

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JOURNAL OF SAFETY RESEARCH 2022年 83卷 269-281页

作者： Pagany, Raphaela Deggendorf Inst Technol Inst Appl Informat Freyung Germany Univ Salzburg Interfac Dept Geoinformat Salzburg Austria

Introduction: The technology in the automotive industry is becoming increasingly safer in the age of auto-mated driving, but the number of accidents is still high, especially in wildlife-vehicle collisions (WVCs). To better avoid these accidents, patterns involved in these accidents must be detected. Method: This paper presents a spatiotemporal risk prediction of WVCs, including various road and environmental char-acteristics. A process of data preparation using GIS automated by Python scripts was developed to enable a spatiotemporal link of diverse features for the subsequent predictive data analysis. Different machine learning (ML) approaches were applied-random forest (RF), feedforward neural networks (FNN), and support vector machine classifier (SVM) -including automated ML to predict the risk of WVCs. Therefore, a dataset of approximately 731,000 accidents reported to the police in Bavaria over a period of 10 years (2010-2019) was used. In addition, non-accidents were randomly generated in Python over time and space for the supervised ML processes. As the actual risk probability for WVCs and non-WVCs is not entirely known, the impact of different training ratios between accidents and non -accidents was tested on the risk prediction quality (RPQ) (25%, 50%, 75%, 90% WVCs) of the double -weighted sensitivity and single-weighted specificity rate. Results: The test yielded high mean values of RPQ as an indicator for a suitable WVC prediction. Both RF (86.6%) and FNN (86.7%) were identified as suitable choices for WVC risk prediction in terms of RPQ. The SVM yielded a lower prediction quality, even though acceptable results could be achieved within a shorter runtime. Practical Applications: Spatial transferability was verified since the algorithm was trained on the dataset of Bavaria (excluding Upper Bavaria) and successfully tested in Upper Bavaria. WVC forecasts were also proven through train-ing with datasets from 2010-2017 and in prediction for 2018 and 2019.(c)

关键词： Wildlife -vehicle collision machine learning Spatiotemporal prediction Random forest Neural networks support vector machine classifier

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Identification of transcription factor contexts in literature using machine learning approaches

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BMC BIOINFORMATICS 2008年第Sup3期9卷 S11-S11页

作者： Yang, Hui Nenadic, Goran Keane, John A. Univ Manchester Sch Comp Sci Manchester Lancs England

Background: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While manual literature curation is expensive and labour intensive, the development of semi-automated text mining support is hindered by unavailability of training data. There have been no studies on how existing data sources (e.g. TF-related data from the MeSH thesaurus and GO ontology) or potentially noisy example data (e.g. protein-protein interaction, PPI) could be used to provide training data for identification of TF-contexts in literature. Results: In this paper we describe a text-classification system designed to automatically recognise contexts related to transcription factors in literature. A learning model is based on a set of biological features (e.g. protein and gene names, interaction words, other biological terms) that are deemed relevant for the task. We have exploited background knowledge from existing biological resources (MeSH and GO) to engineer such features. Weak and noisy training datasets have been collected from descriptions of TF-related concepts in MeSH and GO, PPI data and data representing non-protein-function descriptions. Three machine-learning methods are investigated, along with a vote-based merging of individual approaches and/or different training datasets. The system achieved highly encouraging results, with most classifiers achieving an F-measure above 90%. Conclusions: The experimental results have shown that the proposed model can be used for identification of TF-related contexts (i.e. sentences) with high accuracy, with a significantly reduced set of features when compared to traditional bag-of-words approach. The results of considering existing PPI data suggest that there is not as high similarity between TF and PPI contexts as we have expected. We have also shown that existing knowledge sources are useful both for feature engineering and for obtaining noisy

关键词： support vector machine Training Dataset MeSH support vector machine classifier Biological Model

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GO for gene documents

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BMC BIOINFORMATICS 2007年第s9期8卷 S3-S3页

作者： Srinivasan, Padmini Qiu, Xin Ying Univ Iowa Sch Lib & Informat Sci Iowa City IA 52242 USA Univ Iowa Dept Management Sci Iowa City IA 52242 USA

Background: Annotating genes and their products with Gene Ontology codes is an important area of research. One approach is to use the information available about these genes in the biomedical literature. The goal in this paper, based on this approach, is to develop automatic annotation methods that can supplement the expensive manual annotation processes currently in place. Results: Using a set of support vector machines (SVM) classifiers we were able to achieve Fscores of 0.49, 0.41 and 0.33 for codes of the molecular function, cellular component and biological process GO hierarchies respectively. We find that alternative term weighting strategies are not different from each other in performance and feature selection strategies reduce performance. The best thresholding strategy is one where a single threshold is picked for each hierarchy. Hierarchy level is important especially for molecular function and biological process. The cellular component hierarchy stands apart from the other two in many respects. This may be due to fundamental differences in link semantics. This research shows that it is possible to beneficially exploit the hierarchical structures by defining and testing a relaxed criteria for classification correctness. Finally it is possible to build classifiers for codes with very few associated documents but as expected a huge penalty is paid in performance. Conclusion: The GO annotation problem is complex. Several key observations have been made as for example about topic drift that may be important to consider in annotation strategies.

关键词： support vector machine Gene Ontology Feature Selection support vector machine classifier Annotation Problem

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Protein sequence classification using feature hashing

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PROTEOME SCIENCE 2012年第1期10卷 1-8页

作者： Caragea, Cornelia Silvescu, Adrian Mitra, Prasenjit Penn State Univ University Pk PA 16802 USA Naviance Inc Oakland CA USA

Recent advances in next-generation sequencing technologies have resulted in an exponential increase in the rate at which protein sequence data are being acquired. The k-gram feature representation, commonly used for protein sequence classification, usually results in prohibitively high dimensional input spaces, for large values of k. Applying data mining algorithms to these input spaces may be intractable due to the large number of dimensions. Hence, using dimensionality reduction techniques can be crucial for the performance and the complexity of the learning algorithms. In this paper, we study the applicability of feature hashing to protein sequence classification, where the original high-dimensional space is "reduced" by hashing the features into a low-dimensional space, using a hash function, i.e., by mapping features into hash keys, where multiple features can be mapped (at random) to the same hash key, and "aggregating" their counts. We compare feature hashing with the "bag of k-grams" approach. Our results show that feature hashing is an effective approach to reducing dimensionality on protein sequence classification tasks.

关键词： Hash Function support vector machine classifier Latent Dirichlet Allocation Average Mutual Information Latent Semantic Indexing

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Facial Components Extraction and Expression Recognition in Static images 1

Facial Components Extraction and Expression Recognition in S...

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International Conference on Green Computing and Internet of Things (ICGCIoT)

作者： Pukhrambam, Mameeta Das, Arundhati Saha, Ashim NIT Agartala CSE Dept Agartala India

ISBN: (纸本)9781467379106

This paper deals with the emotion recognition in static images. Facial feature extraction plays a very important role in recognizing a particular emotion in humans. In this paper, the facial expressions in humans. i.e., happy, anger, sad, neutral and disgust, are recognized with the help support vector machine classifier. First, a static image is taken. Then, skin region is extracted from that image using Hue Saturation Value. After skin region extraction, the right eye, the left eye and the mouth part are extracted as they are the most important part for facial expression recognition. These processes are done for every images collected in the training set. Then, support vector machine classifier is used to classify which image belongs to which class category by comparing the feature vectors of the trained images. This paper produces a model which predicts a set of testing images into which class categories the image belongs to, namely anger, disgust, fear, happy and neutral.

关键词： Facial feature extraction support vector machine classifier Hue Saturation Value

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