poster

A data reduction and organization approach for efficient image annotation

Authors:

Priscila T. M. Saito,

Pedro J. de Rezende,

Alexandre X. Falcão,

Celso T. N. Suzuki,

Jancarlo F. GomesAuthors Info & Claims

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

Pages 53 - 57

https://doi.org/10.1145/2480362.2480374

Published: 18 March 2013 Publication History

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Abstract

The labor-intensive and time-consuming process of annotating data is a serious bottleneck in many pattern recognition applications when handling massive datasets. Active learning strategies have been sought to reduce the cost on human annotation, by means of automatically selecting the most informative unlabeled samples for annotation. The critical issue lies on the selection of such samples. As an effective solution, we propose an active learning approach that preprocesses the dataset, efficiently reduces and organizes a learning set of samples and selects the most representative ones for human annotation. Experiments performed on real datasets show that the proposed approach requires only a few iterations to achieve high accuracy, keeping user involvement to a minimum.

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Cited By

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Inácio SGomes JFalcão AMartins dos Santos BSoares FNery Loiola SRosa SNagase Suzuki CBresciani K(2021)Automated Diagnostics: Advances in the Diagnosis of Intestinal Parasitic Infections in Humans and AnimalsFrontiers in Veterinary Science10.3389/fvets.2021.7154068Online publication date: 23-Nov-2021
https://doi.org/10.3389/fvets.2021.715406
Gu YXue HYang J(2017)Cross-Modal Saliency Correlation for Image AnnotationNeural Processing Letters10.1007/s11063-016-9511-445:3(777-789)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s11063-016-9511-4
Gu YQian XLi QWang MHong RTian Q(2015)Image Annotation by Latent Community Detection and Multikernel LearningIEEE Transactions on Image Processing10.1109/TIP.2015.244350124:11(3450-3463)Online publication date: Nov-2015
https://doi.org/10.1109/TIP.2015.2443501
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Index Terms

A data reduction and organization approach for efficient image annotation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
      2. Logical and relational learning
        Inductive logic learning
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Multimedia information systems
      1. Multimedia databases

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Information

Published In

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

March 2013

2124 pages

ISBN:9781450316569

DOI:10.1145/2480362

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
José Carlos Maldonado
ICMC - University of São Paulo, Brazil

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2013

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SAC '13

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SIGAPP

SAC '13: SAC '13

March 18 - 22, 2013

Coimbra, Portugal

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SAC '13 Paper Acceptance Rate 255 of 1,063 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

View all

Inácio SGomes JFalcão AMartins dos Santos BSoares FNery Loiola SRosa SNagase Suzuki CBresciani K(2021)Automated Diagnostics: Advances in the Diagnosis of Intestinal Parasitic Infections in Humans and AnimalsFrontiers in Veterinary Science10.3389/fvets.2021.7154068Online publication date: 23-Nov-2021
https://doi.org/10.3389/fvets.2021.715406
Gu YXue HYang J(2017)Cross-Modal Saliency Correlation for Image AnnotationNeural Processing Letters10.1007/s11063-016-9511-445:3(777-789)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s11063-016-9511-4
Gu YQian XLi QWang MHong RTian Q(2015)Image Annotation by Latent Community Detection and Multikernel LearningIEEE Transactions on Image Processing10.1109/TIP.2015.244350124:11(3450-3463)Online publication date: Nov-2015
https://doi.org/10.1109/TIP.2015.2443501
Saito PSuzuki CGomes Jde Rezende PFalcão A(2015)Robust active learning for the diagnosis of parasitesPattern Recognition10.1016/j.patcog.2015.05.02048:11(3572-3583)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1016/j.patcog.2015.05.020
Costa KPereira LNakamura RPereira CPapa JXavier Falcão A(2015)A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networksInformation Sciences: an International Journal10.1016/j.ins.2014.09.025294:C(95-108)Online publication date: 10-Feb-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.09.025
Vargas JSaito PFalcão ARezende PSantos J(2014)Superpixel-Based Interactive Classification of Very High Resolution ImagesProceedings of the 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images10.1109/SIBGRAPI.2014.49(173-179)Online publication date: 26-Aug-2014
https://dl.acm.org/doi/10.1109/SIBGRAPI.2014.49
Amorim WFalcão ACarvalho M(2014)Semi-supervised Pattern Classification Using Optimum-Path ForestProceedings of the 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images10.1109/SIBGRAPI.2014.45(111-118)Online publication date: 26-Aug-2014
https://dl.acm.org/doi/10.1109/SIBGRAPI.2014.45
Saito Pde Rezende PFalcão ASuzuki CGomes J(2014)An active learning paradigm based on a priori data reduction and organizationExpert Systems with Applications10.1016/j.eswa.2014.04.00741:14(6086-6097)Online publication date: Oct-2014
https://doi.org/10.1016/j.eswa.2014.04.007

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Active learning in multimedia annotation and retrieval: A survey

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