Semi-supervised learning for multi-label image classification with multiple category label predictors
| 000 | 00000nam c2200205 c 4500 | |
| 001 | 000045978863 | |
| 005 | 20190416162923 | |
| 007 | ta | |
| 008 | 181227s2019 ulkad bmAC 000c eng | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 041 | 0 | ▼a eng ▼b kor |
| 085 | 0 | ▼a 0510 ▼2 KDCP |
| 090 | ▼a 0510 ▼b 6D36 ▼c 1094 | |
| 100 | 1 | ▼a 한수현 ▼g 韓守鉉 |
| 245 | 1 0 | ▼a Semi-supervised learning for multi-label image classification with multiple category label predictors / ▼d Soo-hyun Han |
| 260 | ▼a Seoul : ▼b Graduate School, Korea Unversity, ▼c 2019 | |
| 300 | ▼a v, 29장 : ▼b 천연색삽화, 도표 ; ▼c 26 cm | |
| 500 | ▼a 지도교수: 이성환 | |
| 502 | 0 | ▼a 학위논문(석사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2019. 2 |
| 504 | ▼a 참고문헌: 장 24-29 | |
| 530 | ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf) | |
| 653 | ▼a multi label image classification | |
| 776 | 0 | ▼t Semi-Supervised Learning for Multi-Label Image Classification with Multiple Category Label Predictors ▼w (DCOLL211009)000000083459 |
| 900 | 1 0 | ▼a Han, Soo-hyun, ▼e 저 |
| 900 | 1 0 | ▼a 이성환 ▼g 李晟瑍, ▼e 지도교수 |
| 945 | ▼a KLPA |
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| 1 | Semi-supervised learning for multi-label image classification with multiple category label predictors (31회 열람) |
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Contents information
Abstract
In this paper, we focus on learning multi label image classifier using only single label image dataset. Because a multi-label dataset requires annotation of all objects and concepts present in the image, it is time-consuming and effort-consuming to create a dataset, and most datasets that are readily available in real life are mostly single-label datasets. In this paper, we describe how to learn the Convolutional Neural Network (CNN) model using only single-labeled images, without creating or retrieving direct multiple label data. We propose a Multiple Category Label Predictor (MCLP) to predict multiple labels from a single label image set in order to learn CNN as a multi-label prediction model with a single label image set alone. MCLP divides a set of single-label images into N categories, and then learns each category independently. The Category Label Predictor predicts the label of each category, and eventually performs the multi-label image classifier learning using the labels with high probability in all Category Label Predictors. We also propose a new loss function for learning of a multi-label image classifier using a single-label dataset as proposed. We conducted our experiments using our LG Image Dataset to evaluate the proposed method.
Table of Contents
1. Introduction 1 2. Related Work 3 3. Method 4 3.1. Convolutional Neural Network 5 3.1.1. MobileNetV2 5 3.1.1.1. Depthwise Separable Convolution 6 3.1.1.2. Hyper Parameters : Width & Resolution Multiplier 8 3.2. Model Architecture 9 3.3. Inference 11 3.4. Loss Function 12 4. Experiments 14 4.1. Dataset 14 4.2. MCLP’s Hyper Parameters 15 4.3. Main CNN Model’s Hyper Parameters 15 4.4. Result 16 5. Conclusion 24
