Automatic Image Annotation Using a Semi-Supervised and Hierarchical Approach
Date Issued
2007
Date
2007
Author(s)
Hung, Ming-Wei
DOI
en-US
Abstract
Retrieving images by textual queries requires some knowledge of the semantics of the image. Hence we need to find the label words that describe the content of the image and take them as the annotation of the image. Here, we propose an approach to annotate images with user feedback, and it annotates a label a time. The process contains a loop, and it will report a number of images which are most likely to be associated with the label word for user to annotate every iteration. The way to estimate the possibility that an image is associated with a label is using the known labeled images and some unlabeled images as training data to train a classifier for the label. While training the classifier, we use the semi-supervised learning method with unlabeled images to build hierarchical classifiers. The unlabeled images can help clustering while we only have a few labeled training images. After training the classifier, we take the unlabeled image as the input of the classifiers to estimate the confidence values representing the possibility that the image is associated with the label. After using the approach with every label words, we can get the annotation from all of the label words.
Subjects
影像標註
影像檢索
半指導式機器學習方法
階層式分類器
使用者反饋
Image annotation
Image retrieval
semi-supervised learning
hierarchical classifier
user feedback
Type
thesis
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