Semi-supervised deep rule-based approach for image classification
نویسندگان
چکیده
منابع مشابه
Graph-based multimodal semi-supervised image classification
We investigate an image classification task where training images come along with tags, but only a subset being labeled, and the goal is to predict the class label of test images without tags. This task is important for image search engine on photo sharing websites. In previous studies, it is handled by first training a multiple kernel learning classifier using both image content and tags to sc...
متن کاملSemi-Supervised Deep Metrics for Image Registration
Deep metrics have been shown effective as similarity measures in multi-modal image registration; however, the metrics are currently constructed from aligned image pairs in the training data. In this paper, we propose a strategy for learning such metrics from roughly aligned training data. Symmetrizing the data corrects bias in the metric that results from misalignment in the data (at the expens...
متن کاملSemi-supervised learning for image classification
Object class recognition is an active topic in computer vision still presenting many challenges. In most approaches, this task is addressed by supervised learning algorithms that need a large quantity of labels to perform well. This leads either to small datasets (< 10, 000 images) that capture only a subset of the real-world class distribution (but with a controlled and verified labeling proce...
متن کاملHyperspectral Image Classification Based on Semi-Supervised Rotation Forest
Ensemble learning is widely used to combine varieties of weak learners in order to generate a relatively stronger learner by reducing either the bias or the variance of the individual learners. Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image classification by promoting the diversity of base classifiers since...
متن کاملAnt Based Semi-supervised Classification
Semi-supervised classification methods make use of the large amounts of relatively inexpensive available unlabeled data along with the small amount of labeled data to improve the accuracy of the classification. This article presents a novel ‘self-training’ based semi-supervised classification algorithm using the property of aggregation pheromone found in natural behavior of real ants. The propo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Soft Computing
سال: 2018
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2018.03.032