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Predicting foreground object ambiguity and efficiently crowdsourcing the segmentation(s) / Danna Gurari in International journal of computer vision, vol 126 n° 7 (July 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 7 (July 2018) . - pp 714 - 730 Titre : Predicting foreground object ambiguity and efficiently crowdsourcing the segmentation(s) Type de document : Article/Communication Auteurs : Danna Gurari, Auteur ; Kun He, Auteur ; Bo Xiong, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 714 - 730 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] détection d'objet [Termes IGN] réalité de terrain [Termes IGN] segmentation d'image [Termes IGN] zone saillante 3D Résumé : (Auteur) We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems. Specifically, we distinguish between images which lead multiple annotators to segment different foreground objects (ambiguous) versus minor inter-annotator differences of the same object. Taking images from eight widely used datasets, we crowdsource labeling the images as “ambiguous” or “not ambiguous” to segment in order to construct a new dataset we call STATIC. Using STATIC, we develop a system that automatically predicts which images are ambiguous. Experiments demonstrate the advantage of our prediction system over existing saliency-based methods on images from vision benchmarks and images taken by blind people who are trying to recognize objects in their environment. Finally, we introduce a crowdsourcing system to achieve cost savings for collecting the diversity of all valid “ground truth” foreground object segmentations by collecting extra segmentations only when ambiguity is expected. Experiments show our system eliminates up to 47% of human effort compared to existing crowdsourcing methods with no loss in capturing the diversity of ground truths. Numéro de notice : A2018-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1065-7 Date de publication en ligne : 05/02/2018 En ligne : https://doi.org/10.1007/s11263-018-1065-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90887 [article]