Visualization-Based Active Learning for Video Annotation

Liao, Hongsen; Chen, Li; Song, Yibo; Hao, Ming

doi:10.1109/tmm.2016.2614227

Cited by 27 publications

(13 citation statements)

References 29 publications

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“…While active learning focuses on algorithmic selection strategies, the selection may also involve user feedback [53]. For example, projection scatterplots with isocontours visualizing label uncertainty have been used to assist users in data selection [43].…”

Section: Related Work 21 Data Labeling Toolsmentioning

confidence: 99%

“…Data labeling typically involves human annotators. Many data labeling tools have been built to enable annotators to label data objects of various types for various learning tasks (e.g., classification [36,53], segmentation [2]) in different application domains (e.g., image [19,64], text [18,62], video [21,43]). These tools are generally built for a specific labeling task.…”

Section: Introductionmentioning

confidence: 99%

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OneLabeler: A Flexible System for Building Data Labeling Tools

Zhang,

Wang,

Zhang

et al. 2022

Preprint

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Labeled datasets are essential for supervised machine learning. Various data labeling tools have been built to collect labels in different usage scenarios. However, developing labeling tools is timeconsuming, costly, and expertise-demanding on software development. In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios. The framework consists of common modules and states in labeling tools summarized through coding of existing tools. OneLabeler supports configuration and composition of common software modules through visual programming to build data labeling tools. A module can be a human, machine, or mixed computation procedure in data labeling. We demonstrate the expressiveness and utility of the system through ten example labeling tools built with OneLabeler. A user study with developers provides evidence that OneLabeler supports efficient building of diverse data labeling tools.

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Section: Related Work 21 Data Labeling Toolsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OneLabeler: A Flexible System for Building Data Labeling Tools

Zhang,

Wang,

Zhang

et al. 2022

Preprint

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“…The text data is visualized by t-SNE [13], force-directed graph layout and chord diagrams. Hongsen et al [9] used semi-supervised metric learning to train a visualization of video data. In both approaches, the data is displayed next to the scatter plot for labeling.…”

Section: Motivationmentioning

confidence: 99%

Active Learning for Image Recognition Using a Visualization-Based User Interface

Limberg

Krieger

Wersing³

et al. 2019

Lecture Notes in Computer Science

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This paper introduces a novel approach for querying samples to be labeled in active learning for image recognition. The user is able to efficiently label images with a visualization for training a classifier. This visualization is achieved by using dimension reduction techniques to create a 2D feature embedding from high-dimensional features. This is made possible by a querying strategy specifically designed for the visualization, seeking optimized bounding-box views for subsequent labeling. The approach is implemented in a web-based prototype. It is compared in-depth to other active learning querying strategies within a user study we conducted with 31 participants on a challenging data set. While using our approach, the participants could train a more accurate classifier than with the other approaches. Additionally, we demonstrate that due to the visualization, the number of labeled samples increases and also the label quality improves.

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“…Regarding the video annotation task, a visualization‐based AL strategy was devised in the work of Liao et al Since being compatible with more than one measures is not always feasible, an isocontour‐based scatterplot is modified suitably for depicting each time the annotator interacts with an example both its representativeness and informativeness, encouraging wiser choices of annotations . During the process of conducted experiments, the proposed method outreached some SVM‐based AL approaches, taking advantage of its adaptive framework that is provided to the user.…”

Section: Active Learning Theory and Related Workmentioning

confidence: 99%

Active learning Rotation Forest for multiclass classification

Kazllarof

Karlos

Kotsiantis

2019

Computational Intelligence

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Although the achievements of the computer science field have facilitated the tasks of collecting, storing, and accessing vast amounts of data efficiently, its annotation still remains a non–easily resolvable problem since no automated mechanism can perform reliably enough. This fact is even more profound when the objective is the high quality of generalization ability. Active learning constitutes a scheme that is exploited for tackling such problems, controlling the demanded human effort under a trade‐off assumption concerning the achievement of higher accuracy rates. In this work, the well‐known Rotation Forest algorithm is integrated with the active learning theory for constructing a robust and accurate classifier. Thus, apart from exploiting the collected labeled data, unlabeled data mining takes place through appropriate queries, whereas human expert decisions over the most questionable of them enrich the initial ones. Comprehensive comparisons of the proposed algorithm against four distinct learners inside the same learning scheme were executed. Moreover, the baseline strategy of random sampling and the corresponding supervised scenarios were included. During the evaluation stage, 13 publicly available multiclass datasets were assessed, and the obtained results verified our assumptions, regarding also the significant supremacy of the proposed algorithm against the majority of its rivals.

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Visualization-Based Active Learning for Video Annotation

Cited by 27 publications

References 29 publications

OneLabeler: A Flexible System for Building Data Labeling Tools

OneLabeler: A Flexible System for Building Data Labeling Tools

Active Learning for Image Recognition Using a Visualization-Based User Interface

Active learning Rotation Forest for multiclass classification

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