A Study on an Effective Feature Selection Method Using Hog-Family Feature for Human Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F14%3A43886126" target="_blank" >RIV/44555601:13440/14:43886126 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sersc.org/journals/IJMUE/vol9_no12_2014/19.pdf" target="_blank" >http://www.sersc.org/journals/IJMUE/vol9_no12_2014/19.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14257/ijmue.2014.9.12.19" target="_blank" >10.14257/ijmue.2014.9.12.19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Study on an Effective Feature Selection Method Using Hog-Family Feature for Human Detection

Popis výsledku v původním jazyce

It is the important that Support Vector Machine (SVM) is the powerful learning machines and has been applied to varying task with generally acceptable performance. The SVM success for classification tasks in one domain is affected by features that it represents the instance of specific class. The representative and discriminative features that they are given, SVM learning is going to provide better generalization and consequently that we are able to obtain good classifier. In this paper, we define the problem of feature choices for tasks of human detections and measure the performance of each feature. And also we consider HOG-family feature to study an effective feature selection method. Finally we proposed the multi-scale HOG as a NEW family member inthis feature group. In addition we also combine SVM with Principal Component Analysis (PCA) to reduce dimension of features and enhance the evaluation speed while retaining most of discriminative feature vectors.

Název v anglickém jazyce

A Study on an Effective Feature Selection Method Using Hog-Family Feature for Human Detection

Popis výsledku anglicky

It is the important that Support Vector Machine (SVM) is the powerful learning machines and has been applied to varying task with generally acceptable performance. The SVM success for classification tasks in one domain is affected by features that it represents the instance of specific class. The representative and discriminative features that they are given, SVM learning is going to provide better generalization and consequently that we are able to obtain good classifier. In this paper, we define the problem of feature choices for tasks of human detections and measure the performance of each feature. And also we consider HOG-family feature to study an effective feature selection method. Finally we proposed the multi-scale HOG as a NEW family member inthis feature group. In addition we also combine SVM with Principal Component Analysis (PCA) to reduce dimension of features and enhance the evaluation speed while retaining most of discriminative feature vectors.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Multimedia and Ubiquitous Engineering

ISSN

1975-0080

e-ISSN

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Svazek periodika

9

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

KR - Korejská republika

Počet stran výsledku

9

Strana od-do

203-212

Kód UT WoS článku

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EID výsledku v databázi Scopus

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