In many real-world tasks of image classification, limited amounts of labeled data are available to train automatic classifiers. Consequently, extensive human expert involvement is required for verification. A novel solution is presented that makes use of active learning combined with an ensemble of classifiers for each class. The result is a significant reduction in required expert involvement for uncertain image region classification.
Part 6: 10th Mining Humanistic Data Workshop (MHDW 2021)International audienceAs technology progress...
In recent years, several studies have been published about the smart definition of training set usin...
In this paper, we present SALIC, an active learning method for selecting the most appropriate user t...
Recently active learning has attracted a lot of attention in computer vision field, as it is time an...
In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The...
The manual labeling of natural images is and has always been painstaking and slow process, especiall...
© 2016 IEEE. In this paper, we propose a novel cross-media active learning algorithm to reduce the e...
Classifying large datasets without any a-priori information poses a problem in numerous tasks. Espec...
Defining an efficient training set is one of the most delicate phases for the success of remote sens...
This chapter focuses on the development of an active learning approach to an image min-ing problem f...
In the past few years, complex neural networks have achieved state of the art results in image class...
In this letter, we show how active learning can be particularly promising for classifying remote sen...
Nowadays, the inexpensive memory space promotes an accelerating growth of stored image data. To expl...
Defining an efficient training set is one of the most delicate phases for the success of remote sens...
Sufficient supervised information is crucial for any machine learning models to boost performance. H...
Part 6: 10th Mining Humanistic Data Workshop (MHDW 2021)International audienceAs technology progress...
In recent years, several studies have been published about the smart definition of training set usin...
In this paper, we present SALIC, an active learning method for selecting the most appropriate user t...
Recently active learning has attracted a lot of attention in computer vision field, as it is time an...
In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The...
The manual labeling of natural images is and has always been painstaking and slow process, especiall...
© 2016 IEEE. In this paper, we propose a novel cross-media active learning algorithm to reduce the e...
Classifying large datasets without any a-priori information poses a problem in numerous tasks. Espec...
Defining an efficient training set is one of the most delicate phases for the success of remote sens...
This chapter focuses on the development of an active learning approach to an image min-ing problem f...
In the past few years, complex neural networks have achieved state of the art results in image class...
In this letter, we show how active learning can be particularly promising for classifying remote sen...
Nowadays, the inexpensive memory space promotes an accelerating growth of stored image data. To expl...
Defining an efficient training set is one of the most delicate phases for the success of remote sens...
Sufficient supervised information is crucial for any machine learning models to boost performance. H...
Part 6: 10th Mining Humanistic Data Workshop (MHDW 2021)International audienceAs technology progress...
In recent years, several studies have been published about the smart definition of training set usin...
In this paper, we present SALIC, an active learning method for selecting the most appropriate user t...