Inter-active learning of ad-hoc classifiers for video visual analytics

Höferlin, Benjamin; Netzel, Rudolf; Höferlin, Markus; Weiskopf, Daniel; Heidemann, Gunther

doi:10.1109/vast.2012.6400492

Cited by 74 publications

(62 citation statements)

References 35 publications

(45 reference statements)

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“…Heimerl et al [40] proposed to tightly integrate the user into the labelling process and suggested an interactive binary classifier training approach for text analysis. Höferlin et al [41] presented a system to build cascades of linear classifiers for image classification.…”

Section: Visual Classificationmentioning

confidence: 99%

EasySVM: A visual analysis approach for open-box support vector machines

Chen

et al. 2017

Comp. Visual Media

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Support vector machines (SVMs) are supervised learning models traditionally employed for classification and regression analysis. In classification analysis, a set of training data is chosen, and each instance in the training data is assigned a categorical class. An SVM then constructs a model based on a separating plane that maximizes the margin between different classes. Despite being one of the most popular classification models because of its strong performance empirically, understanding the knowledge captured in an SVM remains difficult. SVMs are typically applied in a black-box manner where the details of parameter tuning, training, and even the final constructed model are hidden from the users. This is natural since these details are often complex and difficult to understand without proper visualization tools. However, such an approach often brings about various problems including trial-and-error tuning and suspicious users who are forced to trust these models blindly.The contribution of this paper is a visual analysis approach for building SVMs in an open-box manner. Our goal is to improve an analyst's understanding of the SVM modeling process through a suite of visualization techniques that allow users to have full interactive visual control over the entire SVM training process. Our visual exploration tools have been developed to enable intuitive parameter tuning, training data 2016-12-24; accepted: 2017-01-09 manipulation, and rule extraction as part of the SVM training process. To demonstrate the efficacy of our approach, we conduct a case study using a real-world robot control dataset.

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Section: Visual Classificationmentioning

confidence: 99%

EasySVM: A visual analysis approach for open-box support vector machines

Chen

et al. 2017

Comp. Visual Media

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“…One of the first systems doing so is the Informedia system which employs speech and video analysis in conjunction with effective user interfaces [7]. A system targeting computer vision algorithm developers is presented in [16] allowing the user to gain insight in features and how to use these features in surveillance. Canopy [3] is an advanced system combining text analysis, various visualizations, and visual similarity based matching to explore visual collections.…”

Section: A Multimedia Visualization and Analyticsmentioning

confidence: 99%

Multimedia Pivot Tables for Multimedia Analytics on Image Collections

Worring

Koelma

Zahálka

2016

IEEE Trans. Multimedia

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Abstract-We propose a multimedia analytics solution for getting insight in image collections by extending the powerful analytic capabilities of pivot tables, found in the ubiquitous spreadsheets, to multimedia. We formalize the concept of multimedia pivot tables and give design rules and methods for the multimodal summarization, structuring, and browsing of the collection based on these tables, all optimized to support an analyst in getting structural and conclusive insights. Our proposed solution provides truly interactive analytics on the visual content of image collections through concept detection results, as well as tags, geolocation, time and other metadata. We have performed user experiments with novice users on a dataset from Flickr to improve the initial design and with expert users in marketing and multimedia analysis on two domain specific datasets collected from Instagram. The results show that analysts are indeed capable of deriving structural and conclusive insights using the proposed multimedia analytics solution. On our website videos of the system in action are available.

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“…Finally, an inter-active learning system [16] provides feedback on classification quality to users by means of a set of integrated cascaded scatter plots of the instances class distribution, in each stage of the classifier. Annotated instances are also organized by their similarity, using a tdistributed stochastic neighbor embedding (tSNE) [47].…”

Section: Visual Data Classificationmentioning

confidence: 99%

An Approach to Supporting Incremental Visual Data Classification

Paiva

Schwartz

Pedrini

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Abstract-Automatic data classification is a computationally intensive task that presents variable precision and is considerably sensitive to the classifier configuration and to data representation, particularly for evolving data sets. Some of these issues can best be handled by methods that support users' control over the classification steps. In this paper, we propose a visual data classification methodology that supports users in tasks related to categorization such as training set selection; model creation, application and verification; and classifier tuning. The approach is then well suited for incremental classification, present in many applications with evolving data sets. Data set visualization is accomplished by means of point placement strategies, and we exemplify the method through multidimensional projections and Neighbor Joining trees. The same methodology can be employed by a user who wishes to create his or her own ground truth (or perspective) from a previously unlabeled data set. We validate the methodology through its application to categorization scenarios of image and text data sets, involving the creation, application, verification, and adjustment of classification models.

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Inter-active learning of ad-hoc classifiers for video visual analytics

Cited by 74 publications

References 35 publications

EasySVM: A visual analysis approach for open-box support vector machines

EasySVM: A visual analysis approach for open-box support vector machines

Multimedia Pivot Tables for Multimedia Analytics on Image Collections

An Approach to Supporting Incremental Visual Data Classification

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