图 书 馆
用于图像分类的多模式半监督学习Multimodal semi-supervised learning for image classification |
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| 课程网址: | http://videolectures.net/cvpr2010_guillaumin_mssl/ |
| 主讲教师: | Matthieu Guillaumin |
| 开课单位: | 印度格勒诺布尔-罗纳-阿尔卑斯 |
| 开课时间: | 2010-07-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In image categorization the goal is to decide if an image belongs to a certain category or not. A binary classifier can be learned from manually labeled images; while using more labeled examples improves performance, obtaining the image labels is a time consuming process. We are interested in how other sources of information can aid the learning process given a fixed amount of labeled images. In particular, we consider a scenario where keywords are associated with the training images, e.g. as found on photo sharing websites. The goal is to learn a classifier for images alone, but we will use the keywords associated with labeled and unlabeled images to improve the classifier using semi-supervised learning. We first learn a strong Multiple Kernel Learning (MKL) classifier using both the image content and keywords, and use it to score unlabeled images. We then learn classifiers on visual features only, either support vector machines (SVM) or leastsquares regression (LSR), from the MKL output values on both the labeled and unlabeled images. In our experiments on 20 classes from the PASCAL VOC’07 set and 38 from the MIR Flickr set, we demonstrate the benefit of our semi-supervised approach over only using the labeled images. We also present results for a scenario where we do not use any manual labeling but directly learn classifiers from the image tags. The semi-supervised approach also improves classification accuracy in this case. |
| 关 键 词: | 图像分类; 监督学习; 学习模式 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-06-07：chenxin01 |
| 最后编审: | 2023-06-07：chenxin01 |
| 阅读次数: | 20 |