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      LOST: A flexible framework for semi-automatic image annotation

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          Abstract

          State-of-the-art computer vision approaches rely on huge amounts of annotated data. The collection of such data is a time consuming process since it is mainly performed by humans. The literature shows that semi-automatic annotation approaches can significantly speed up the annotation process by the automatic generation of annotation proposals to support the annotator. In this paper we present a framework that allows for a quick and flexible design of semi-automatic annotation pipelines. We show that a good design of the process will speed up the collection of annotations. Our contribution is a new approach to image annotation that allows for the combination of different annotation tools and machine learning algorithms in one process. We further present potential applications of our approach. The source code of our framework called LOST (Label Objects and Save Time) is available at: https://github.com/l3p-cv/lost.

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          LabelMe: A Database and Web-Based Tool for Image Annotation

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            Crowdsourcing in Computer Vision

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              ViTBAT: Video tracking and behavior annotation tool

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                Author and article information

                Journal
                16 October 2019
                Article
                1910.07486
                57ba5b55-3099-4794-ba32-d03f044c80fe

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                68T45
                cs.CV

                Computer vision & Pattern recognition
                Computer vision & Pattern recognition

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