User Preference and Performance using ...
Document type :
Communication dans un congrès avec actes
DOI :
Title :
User Preference and Performance using Tagging and Browsing for Image Labeling
Author(s) :
Fruchard, Bruno [Auteur]
Technology and knowledge for interaction [LOKI]
Malacria, Sylvain [Auteur]
Technology and knowledge for interaction [LOKI]
Casiez, Géry [Auteur]
Institut universitaire de France [IUF]
Technology and knowledge for interaction [LOKI]
Huot, Stephane [Auteur]
Technology and knowledge for interaction [LOKI]
Technology and knowledge for interaction [LOKI]
Malacria, Sylvain [Auteur]

Technology and knowledge for interaction [LOKI]
Casiez, Géry [Auteur]

Institut universitaire de France [IUF]
Technology and knowledge for interaction [LOKI]
Huot, Stephane [Auteur]

Technology and knowledge for interaction [LOKI]
Conference title :
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI ’23)
City :
Hambourg
Country :
Allemagne
Start date of the conference :
2023-04-23
Book title :
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI ’23)
English keyword(s) :
Human-Computer Interaction
empirical studies
user performance
image labeling
tagging
browsing
visual complexity
open science
empirical studies
user performance
image labeling
tagging
browsing
visual complexity
open science
HAL domain(s) :
Informatique [cs]/Interface homme-machine [cs.HC]
English abstract : [en]
Visual content must be labeled to facilitate navigation and retrieval, or provide ground truth data for supervised machine learning approaches. The efficiency of labeling techniques is crucial to produce numerous qualitative ...
Show more >Visual content must be labeled to facilitate navigation and retrieval, or provide ground truth data for supervised machine learning approaches. The efficiency of labeling techniques is crucial to produce numerous qualitative labels, but existing techniques remain sparsely evaluated. We systematically evaluate the efficiency of tagging and browsing tasks in relation to the number of images displayed, interaction modes, and the image visual complexity. Tagging consists in focusing on a single image to assign multiple labels (image-oriented strategy), and browsing in focusing on a single label to assign to multiple images (label-oriented strategy). In a first experiment, we focus on the nudges inducing participants to adopt one of the strategies (n=18). In a second experiment, we evaluate the efficiency of the strategies (n=24). Results suggest an image- oriented strategy (tagging task) leads to shorter annotation times, especially for complex images, and participants tend to adopt it regardless of the conditions they face.Show less >
Show more >Visual content must be labeled to facilitate navigation and retrieval, or provide ground truth data for supervised machine learning approaches. The efficiency of labeling techniques is crucial to produce numerous qualitative labels, but existing techniques remain sparsely evaluated. We systematically evaluate the efficiency of tagging and browsing tasks in relation to the number of images displayed, interaction modes, and the image visual complexity. Tagging consists in focusing on a single image to assign multiple labels (image-oriented strategy), and browsing in focusing on a single label to assign to multiple images (label-oriented strategy). In a first experiment, we focus on the nudges inducing participants to adopt one of the strategies (n=18). In a second experiment, we evaluate the efficiency of the strategies (n=24). Results suggest an image- oriented strategy (tagging task) leads to shorter annotation times, especially for complex images, and participants tend to adopt it regardless of the conditions they face.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Collections :
Source :
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