An Image Classification Method based on Improved Active Learning and Semi-supervised Learning Algorithm

2018 ◽  
Vol 11 (3) ◽  
pp. 348-358
Author(s):  
Dan Luo
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Classification Method

scholarly journals Active Learning Plus Deep Learning Can Establish Cost-Effective and Robust Model for Multichannel Image: A Case on Hyperspectral Image Classification

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4975
Author(s):  
Fangyu Shi ◽  
Zhaodi Wang ◽  
Menghan Hu ◽  
Guangtao Zhai
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Image Classification ◽  
Large Scale ◽  
Hyperspectral Image ◽  
Image Annotation ◽  
Learning Algorithm ◽  
Magnetic Resonance Images ◽  
Biological Engineering ◽  
Hyperspectral Image Classification

Relying on large scale labeled datasets, deep learning has achieved good performance in image classification tasks. In agricultural and biological engineering, image annotation is time-consuming and expensive. It also requires annotators to have technical skills in specific areas. Obtaining the ground truth is difficult because natural images are expensive. In addition, images in these areas are usually stored as multichannel images, such as computed tomography (CT) images, magnetic resonance images (MRI), and hyperspectral images (HSI). In this paper, we present a framework using active learning and deep learning for multichannel image classification. We use three active learning algorithms, including least confidence, margin sampling, and entropy, as the selection criteria. Based on this framework, we further introduce an “image pool” to make full advantage of images generated by data augmentation. To prove the availability of the proposed framework, we present a case study on agricultural hyperspectral image classification. The results show that the proposed framework achieves better performance compared with the deep learning model. Manual annotation of all the training sets achieves an encouraging accuracy. In comparison, using active learning algorithm of entropy and image pool achieves a similar accuracy with only part of the whole training set manually annotated. In practical application, the proposed framework can remarkably reduce labeling effort during the model development and upadting processes, and can be applied to multichannel image classification in agricultural and biological engineering.

A Semi-Supervised Active Learning Framework for Image Classification

2014 ◽  
Vol 556-562 ◽  
pp. 4765-4769
Author(s):  
Han Yi Li ◽  
Ming Yang ◽  
Nan Nan Kang ◽  
Lu Lu Yue
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Supervised Learning ◽  
The Other ◽  
Training Data ◽  
Classification Error ◽  
Classification Algorithms ◽  
Learning Framework ◽  
User Query ◽  
Changing Rate

In this paper, a novel image classification method, incorporating active learning and semi-supervised learning (SSL), is proposed. In this method, two classifiers are needed where one is trained by labeled data and some unlabeled data, while the other one is trained only by labeled data. Specifically, in each round, two classifiers iterate to select useful examples in contention for user query. Then we compute the label changing rate for every unlabeled example in each classifier. Those examples in which the label changing rate is zero and the label in the two classifiers is the same are selected to add into the training data of the first classifier. Our experimental results show that our method significantly reduced the need of labeled examples, while at the same time reducing classification error compared with widely used image classification algorithms.

scholarly journals Pre-Training Acquisition Functions by Deep Reinforcement Learning for Fixed Budget Active Learning

2021 ◽  
Author(s):  
Yusuke Taguchi ◽  
Hideitsu Hino ◽  
Keisuke Kameyama
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
Learning Problem ◽  
Q Learning ◽  
Fixed Budget ◽  
Active Learner

AbstractThere are many situations in supervised learning where the acquisition of data is very expensive and sometimes determined by a user’s budget. One way to address this limitation is active learning. In this study, we focus on a fixed budget regime and propose a novel active learning algorithm for the pool-based active learning problem. The proposed method performs active learning with a pre-trained acquisition function so that the maximum performance can be achieved when the number of data that can be acquired is fixed. To implement this active learning algorithm, the proposed method uses reinforcement learning based on deep neural networks as as a pre-trained acquisition function tailored for the fixed budget situation. By using the pre-trained deep Q-learning-based acquisition function, we can realize the active learner which selects a sample for annotation from the pool of unlabeled samples taking the fixed-budget situation into account. The proposed method is experimentally shown to be comparable with or superior to existing active learning methods, suggesting the effectiveness of the proposed approach for the fixed-budget active learning.

scholarly journals Online Active Learning of Reject Option Classifiers

2020 ◽  
Vol 34 (04) ◽  
pp. 5652-5659
Author(s):  
Kulin Shah ◽  
Naresh Manwani
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Experimental Results ◽  
Reject Option ◽  
Novel Algorithms ◽  
Ramp Loss Function

Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required.

scholarly journals Personalized Image Classification by Semantic Embedding and Active Learning

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1314
Author(s):  
Mofei Song
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Learning Algorithm ◽  
Representation Learning ◽  
Public Image ◽  
Verification Task ◽  
Learning Stage ◽  
Expected Time ◽  
Semantic Embedding ◽  
Deep Model

Currently, deep learning has shown state-of-the-art performance in image classification with pre-defined taxonomy. However, in a more real-world scenario, different users usually have different classification intents given an image collection. To satisfactorily personalize the requirement, we propose an interactive image classification system with an offline representation learning stage and an online classification stage. During the offline stage, we learn a deep model to extract the feature with higher flexibility and scalability for different users’ preferences. Instead of training the model only with the inter-class discrimination, we also encode the similarity between the semantic-embedding vectors of the category labels into the model. This makes the extracted feature adapt to multiple taxonomies with different granularities. During the online session, an annotation task iteratively alternates with a high-throughput verification task. When performing the verification task, the users are only required to indicate the incorrect prediction without giving the exact category label. For each iteration, our system chooses the images to be annotated or verified based on interactive efficiency optimization. To provide a high interactive rate, a unified active learning algorithm is used to search the optimal annotation and verification set by minimizing the expected time cost. After interactive annotation and verification, the new classified images are used to train a customized classifier online, which reflects the user-adaptive intent of categorization. The learned classifier is then used for subsequent annotation and verification tasks. Experimental results under several public image datasets show that our method outperforms existing methods.

scholarly journals AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection

2020 ◽  
Vol 34 (04) ◽  
pp. 3537-3544
Author(s):  
Xu Chen ◽  
Brett Wujek
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Learning System ◽  
Automated Learning ◽  
Benchmark Datasets ◽  
Hyperparameter Selection ◽  
Query Selection

Automated machine learning (AutoML) strives to establish an appropriate machine learning model for any dataset automatically with minimal human intervention. Although extensive research has been conducted on AutoML, most of it has focused on supervised learning. Research of automated semi-supervised learning and active learning algorithms is still limited. Implementation becomes more challenging when the algorithm is designed for a distributed computing environment. With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these challenges. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes in a distributed manner. Subsequently, automated active learning is addressed by jointly optimizing hyperparameters in both the classification and query selection stages leveraging the graph loss minimization and entropy regularization. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data by first partitioning the unlabeled data and replicating the labeled data to different worker nodes in the classification stage, and then aggregating the data in the controller in the query selection stage. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and a real-world electrocardiogram (ECG) dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.

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