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Mining images on semantics via statistical learning

Published: 21 August 2005 Publication History

Abstract

In this paper, we have proposed a novel framework to enable hierarchical image classification via statistical learning. By integrating the concept hierarchy for semantic image concept organization, a hierarchical mixture model is proposed to enable multi-level modeling of semantic image concepts and hierarchical classifier combination. Thus, learning the classifiers for the semantic image concepts at the high level of the concept hierarchy can be effectively achieved by detecting the presences of the relevant base-level atomic image concepts. To effectively learn the base-level classifiers for the atomic image concepts at the first level of the concept hierarchy, we have proposed a novel adaptive EM algorithm to achieve more effective model selection and parameter estimation. In addition, a novel penalty term is proposed to effectively eliminate the misleading effects of the outlying unlabeled images on semi-supervised classifier training. Our experimental results in a specific image domain of outdoor photos are very attractive.

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  • (2014)Ontology based classification for multi-label image annotation2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)10.1109/ICAICTA.2014.7005945(226-231)Online publication date: Aug-2014
  • (2013)Training inter-related classifiers for automatic image classification and annotationPattern Recognition10.1016/j.patcog.2012.10.02946:5(1382-1395)Online publication date: May-2013
  • (2011)Collection-based sparse label propagation and its application on social group suggestion from photosACM Transactions on Intelligent Systems and Technology10.1145/1899412.18994162:2(1-21)Online publication date: 24-Feb-2011
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cover image ACM Conferences
KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
August 2005
844 pages
ISBN:159593135X
DOI:10.1145/1081870
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 2005

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Author Tags

  1. adaptive EM algorithm
  2. hierarchical mixture model
  3. image classification

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

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Cited By

View all
  • (2014)Ontology based classification for multi-label image annotation2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)10.1109/ICAICTA.2014.7005945(226-231)Online publication date: Aug-2014
  • (2013)Training inter-related classifiers for automatic image classification and annotationPattern Recognition10.1016/j.patcog.2012.10.02946:5(1382-1395)Online publication date: May-2013
  • (2011)Collection-based sparse label propagation and its application on social group suggestion from photosACM Transactions on Intelligent Systems and Technology10.1145/1899412.18994162:2(1-21)Online publication date: 24-Feb-2011
  • (2009)Mining Personal Image Collection for Social Group SuggestionProceedings of the 2009 IEEE International Conference on Data Mining Workshops10.1109/ICDMW.2009.77(202-207)Online publication date: 6-Dec-2009
  • (2008)New approach for hierarchical classifier training and multi-level image annotationProceedings of the 14th international conference on Advances in multimedia modeling10.5555/1785794.1785801(45-57)Online publication date: 9-Jan-2008
  • (2008)Image classification based on the bagging-adaboost ensemble2008 IEEE International Conference on Multimedia and Expo10.1109/ICME.2008.4607726(1481-1484)Online publication date: Jun-2008
  • (2008)New Approach for Hierarchical Classifier Training and Multi-level Image AnnotationAdvances in Multimedia Modeling10.1007/978-3-540-77409-9_5(45-57)Online publication date: 2008
  • (2007)Hierarchical classification for automatic image annotationProceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1277741.1277763(111-118)Online publication date: 23-Jul-2007
  • (2006)Incorporating concept ontology to enable probabilistic concept reasoning for multi-level image annotationProceedings of the 8th ACM international workshop on Multimedia information retrieval10.1145/1178677.1178691(79-88)Online publication date: 26-Oct-2006

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