poster

Mining partially annotated images

Authors:

Zhongfei (Mark) Zhang,

Zhengyou ZhangAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1199 - 1207

https://doi.org/10.1145/2020408.2020592

Published: 21 August 2011 Publication History

Abstract

In this paper, we study the problem of mining partially annotated images. We first define what the problem of mining partially annotated images is, and argue that in many real-world applications annotated images are typically partially annotated and thus that the problem of mining partially annotated images exists in many situations. We then propose an effective solution to this problem based on a statistical model we have developed called the Semi-Supervised Correspondence Hierarchical Dirichlet Process (SSCHDP). The main idea of this model lies in exploiting the information pertaining to partially annotated images or even unannotated images to achieve semi-supervised learning under the HDP structure. We apply this model to completing the annotations appropriately for partially annotated images in the training data and then to predicting the annotations appropriately and completely for all the unannotated images either in the training data or in any unseen data beyond the training process. Experiments show that SSC-HDP is superior to the peer models from the recent literature when they are applied to solving the problem of mining partially annotated images.

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Cited By

Hang JZhang M(2024)Dual Perspective of Label-Specific Feature Learning for Multi-Label ClassificationACM Transactions on Knowledge Discovery from Data10.1145/370500619:1(1-30)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3705006
Liu WWang HShen XTsang I(2022)The Emerging Trends of Multi-Label LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2021.311933444:11(7955-7974)Online publication date: 1-Nov-2022
https://doi.org/10.1109/TPAMI.2021.3119334
Li RZhao XShang ZJia L(2022)Semi‐supervised multi‐label learning with missing labels by exploiting feature‐label correlationsStatistical Analysis and Data Mining: The ASA Data Science Journal10.1002/sam.1160716:2(187-209)Online publication date: 31-Dec-2022
https://doi.org/10.1002/sam.11607
Show More Cited By

Index Terms

Mining partially annotated images
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
  2. Information systems applications
    1. Data mining
    2. Multimedia information systems
      1. Multimedia databases

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Published In

cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 ACM.

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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Publication History

Published: 21 August 2011

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KDD '11

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KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

California, San Diego, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Hang JZhang M(2024)Dual Perspective of Label-Specific Feature Learning for Multi-Label ClassificationACM Transactions on Knowledge Discovery from Data10.1145/370500619:1(1-30)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3705006
Liu WWang HShen XTsang I(2022)The Emerging Trends of Multi-Label LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2021.311933444:11(7955-7974)Online publication date: 1-Nov-2022
https://doi.org/10.1109/TPAMI.2021.3119334
Li RZhao XShang ZJia L(2022)Semi‐supervised multi‐label learning with missing labels by exploiting feature‐label correlationsStatistical Analysis and Data Mining: The ASA Data Science Journal10.1002/sam.1160716:2(187-209)Online publication date: 31-Dec-2022
https://doi.org/10.1002/sam.11607
Xu BZeng ZLian CDing Z(2021)Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot LearningIEEE Transactions on Image Processing10.1109/TIP.2021.305067730(2207-2219)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIP.2021.3050677
Ma JChow T(2019)Topic-Based Algorithm for Multilabel Learning With Missing LabelsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2018.287443430:7(2138-2152)Online publication date: Jul-2019
https://doi.org/10.1109/TNNLS.2018.2874434
Chung CDai B(2016)A Framework of the Semi-supervised Multi-label Classification with Non-uniformly Distributed Incomplete LabelsBig Data Analytics and Knowledge Discovery10.1007/978-3-319-43946-4_18(267-280)Online publication date: 6-Aug-2016
https://doi.org/10.1007/978-3-319-43946-4_18
Zhao FGuo Y(2015)Semi-supervised multi-label learning with incomplete labelsProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832747.2832815(4062-4068)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832747.2832815
Yu GRangwala HDomeniconi CZhang GYu Z(2014)Protein function prediction with incomplete annotationsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2013.14211:3(579-591)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1109/TCBB.2013.142
Lin ZDing GHu MLin YSam Ge S(2014)Image tag completion via dual-view linear sparse reconstructionsComputer Vision and Image Understanding10.1016/j.cviu.2014.03.012124(42-60)Online publication date: Jul-2014
https://doi.org/10.1016/j.cviu.2014.03.012
Qi ZYang MZhang ZZhang ZBabaguchi NAizawa KSmith JSatoh SPlagemann THua XYan R(2012)Multi-view learning from imperfect taggingProceedings of the 20th ACM international conference on Multimedia10.1145/2393347.2393416(479-488)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2393347.2393416
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