Multi-class active learning for image classification

Joshi, Ajay; Porikli, Fatih; Papanikolopoulos, Nikolaos

doi:10.1109/cvpr.2009.5206627

Cited by 491 publications

(260 citation statements)

References 10 publications

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“…Some may have larger classification uncertainty than the ones whose entropy may be higher. For the above problem, Joshi et al [6] proposed a more effective active learning sample selection criterion BvSB. This criterion considers the difference between the probability values of the two classes having the highest estimated probability value as a measure of uncertainty, which results in a better performance in practical applications.…”

Section: Related Workmentioning

confidence: 99%

“…We employ the best-versus-second-best (BvSB) [6] approach, which considers the difference between the probability values of the two classes having the highest estimated probability value as a measure of uncertainty. Assume that our estimated probability distribution for a certain example is denoted by P .…”

Section: Proposed Approachmentioning

confidence: 99%

“…Probability value of the best class guess and the second best guess are, respectively. We obtain the BvSB measure and refer [6] for detained information:

\begin{matrix} Uncertainty (x_{i}) \\ = BvSB = \arg \min_{x_{i} \in U} (p (y_{Best} ∣ x_{i}) - p (y_{Second - Best} ∣ x_{i})) . \end{matrix}

…”

Section: Proposed Approachmentioning

confidence: 99%

“…The strategies are generally divided into two categories. One is based on uncertainty sampling [5, 6], which considers samples' uncertainty as information content and selects the most uncertain samples for labeling. Although most uncertainty query selection strategies have a wide range of applications and achieve good results in many circumstances, they fail to take information in the large amount of unlabeled instances into account and are prone to query outliers.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Active Learning Approach with Uncertainty, Representativeness, and Diversity

Zhang

Xin

et al. 2014

The Scientific World Journal

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Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances' uncertainty and representativeness to constitute the most informative set. Then, use the kernel k-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

“…Probability value of the best class guess and the second best guess are, respectively. We obtain the BvSB measure and refer [6] for detained information:

\begin{matrix} Uncertainty (x_{i}) \\ = BvSB = \arg \min_{x_{i} \in U} (p (y_{Best} ∣ x_{i}) - p (y_{Second - Best} ∣ x_{i})) . \end{matrix}

…”

Section: Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Active Learning Approach with Uncertainty, Representativeness, and Diversity

Zhang

Xin

et al. 2014

The Scientific World Journal

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show abstract

“…The most popular approaches include query-by-committee [24, 7, 9] where a number of distinct classification models are generated and an instance having the most disagreement among the classification models in predicting the label is selected for querying. Another popular approach is querying an instance with maximum uncertainty of labeling measured by the distance from the classification boundary [6, 22, 28] or by the entropy in the predicted label [17, 16]. …”

Section: Introductionmentioning

confidence: 99%

Batch mode active sampling based on marginal probability distribution matching

Chattopadhyay

Wang

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Active Learning is a machine learning and data mining technique that selects the most informative samples for labeling and uses them as training data; it is especially useful when there are large amount of unlabeled data and labeling them is expensive. Recently, batch-mode active learning, where a set of samples are selected concurrently for labeling, based on their collective merit, has attracted a lot of attention. The objective of batch-mode active learning is to select a set of informative samples so that a classifier learned on these samples has good generalization performance on the unlabeled data. Most of the existing batch-mode active learning methodologies try to achieve this by selecting samples based on varied criteria. In this paper we propose a novel criterion which achieves good generalization performance of a classifier by specifically selecting a set of query samples that minimizes the difference in distribution between the labeled and the unlabeled data, after annotation. We explicitly measure this difference based on all candidate subsets of the unlabeled data and select the best subset. The proposed objective is an NP-hard integer programming optimization problem. We provide two optimization techniques to solve this problem. In the first one, the problem is transformed into a convex quadratic programming problem and in the second method the problem is transformed into a linear programming problem. Our empirical studies using publicly available UCI datasets and a biomedical image dataset demonstrate the effectiveness of the proposed approach in comparison with the state-of-the-art batch-mode active learning methods. We also present two extensions of the proposed approach, which incorporate uncertainty of the predicted labels of the unlabeled data and transfer learning in the proposed formulation. Our empirical studies on UCI datasets show that incorporation of uncertainty information improves performance at later iterations while our studies on 20 Newsgroups dataset show that transfer learning improves the performance of the classifier during initial iterations.

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Pseudolabel‐guided multiview consensus graph learning for semisupervised classification

Guo

Wang

2022

Int J of Intelligent Sys

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Semisupervised multiview learning gains extensive research attention due to its strong capability to utilize the heterogeneous features and the label information of a few labeled samples. However, the supervision information is not well utilized in the process of exploring the consensus structure of the multiview data. In this paper, we propose a novel unified pseudolabel-guided multiview consensus (PMvC) learning framework for the semisupervised classification problem, which learns the consensus structure of multiview data by fully exploiting the supervised information of labeled samples. Specifically, PMvC first assigns multiple pseudolabels to the unlabeled samples by selecting the nearest labeled sample in each view separately, and then labels the part of unlabeled samples by selecting the pseudolabel that agrees across all views. By doing so, the high-confident pseudolabeled samples can be selected to enlarge the labeled sample pool and the supervision information can be exploited further in the learning process. In addition, to capture the consensus structure of the multiview data, PMvC learns a consensus graph from the view-specific selfrepresentation graph guided by enhanced supervision information, which better preserves the manifold structure of samples. Meanwhile, the label information is also propagated from the labeled samples to the unlabeled samples by the learned consensus graph simultaneously.

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Multi-class active learning for image classification

Cited by 491 publications

References 10 publications

An Active Learning Approach with Uncertainty, Representativeness, and Diversity

An Active Learning Approach with Uncertainty, Representativeness, and Diversity

Batch mode active sampling based on marginal probability distribution matching

Pseudolabel‐guided multiview consensus graph learning for semisupervised classification

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