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Graduate School of Ajou University
Department of Life and Media Cooperation Course
3. Theses(Master)
CNN 을 사용하여 단일 이미지에서 상위 수준의 헤어스타일 속성을 분류하고 회귀 학습
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 신현준 | - |
| dc.contributor.author | 김태현 | - |
| dc.date.accessioned | 2022-11-29T02:32:00Z | - |
| dc.date.available | 2022-11-29T02:32:00Z | - |
| dc.date.issued | 2020-02 | - |
| dc.identifier.other | 29878 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/19488 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :라이프미디어협동과정,2020. 2 | - |
| dc.description.tableofcontents | I Introduction 1 II Related Works 4 A Segmentation Approach 4 B Detection Approach 5 C Convolutional Neural Networks 5 III Method 7 A Network Architectures 7 1 VGG-16 7 2 ResNet-20 8 B Pre-Processing 10 1 Classification Data-Generation 11 2 Regression Data-Generation 12 IV Experiment 19 A Loss Function 20 B Overall Training Process 22 V Result 24 A Hair Length Classification Prediction 24 B Hair Color Regression Prediction 27 VI Limitation and Discussion 33 VII Conclusion 34 References 35 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | CNN 을 사용하여 단일 이미지에서 상위 수준의 헤어스타일 속성을 분류하고 회귀 학습 | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 라이프미디어협동과정 | - |
| dc.date.awarded | 2020. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 1138516 | - |
| dc.identifier.uci | I804:41038-000000029878 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000029878 | - |
| dc.subject.keyword | annotation | - |
| dc.subject.keyword | attributes | - |
| dc.subject.keyword | classification | - |
| dc.subject.keyword | data generation | - |
| dc.subject.keyword | deep convolutional neural network | - |
| dc.subject.keyword | deep learning | - |
| dc.subject.keyword | hair | - |
| dc.subject.keyword | regression | - |
| dc.description.alternativeAbstract | We present a fully automatic framework that classifies and regress the high-level hair attributes specifically length of hair and hair color from a single input image. This research was conducted to reduce the hard labor preparing the desired data for training and learning purpose. Also, this research can expand to different human face analysis research. We created new annotations for ten thousand image data. Which can be used for future research on human face analysis. We developed an automatic file distributor when annotations are liable. We further show the effectiveness of our experiment on cellular phone taken images. | - |
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