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Semantic image classification with hierarchical feature subset selection

Published: 10 November 2005 Publication History

Abstract

High-dimensional visual features for image content characterization enables effective image classification. However, training accurate image classifiers in high-dimensional feature space suffers from the problem of curse of dimensionality and thus requires a large number of labeled images. To achieve accurate classifier training in high-dimensional feature space, we propose a hierarchical feature subset selection algorithm for semantic image classification, where the feature subset selection procedure is seamlessly integrated with the underlying classifier training procedure in a single algorithm. First, our hierarchical feature subset selection framework partitions the high-dimensional feature space into multiple homogeneous feature subspaces and forms a two-level feature hierarchy. Second, weak image classifiers are trained for each homogeneous feature subspace at the lower level of the feature hierarchy, where the traditional feature subset selection techniques such as principal component analysis (PCA) can be used for dimension reduction. Finally, these weak classifiers are boosted to determine an optimal image classifier and the higher-level feature subset selection is realized by selecting the most effective weak classifiers and their corresponding homogeneous feature subsets. Our experiments on a specific domain of natural images have obtained very positive results.

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      cover image ACM Conferences
      MIR '05: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
      November 2005
      274 pages
      ISBN:1595932445
      DOI:10.1145/1101826
      • General Chairs:
      • Hongjiang Zhang,
      • John Smith,
      • Qi Tian
      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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      Published: 10 November 2005

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      Author Tags

      1. classifier training
      2. feature selection
      3. semantic image classification

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      MM&Sec '05
      MM&Sec '05: Multimedia and Security Workshop 2005
      November 10 - 11, 2005
      Hilton, Singapore

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      • (2012)Learning a hierarchical image manifold for Web image classificationJournal of Zhejiang University SCIENCE C10.1631/jzus.C120003213:10(719-735)Online publication date: 10-Oct-2012
      • (2012)Spatio concept relation accompanied interface to bridge semantic gap of video search and retrieval2012 World Congress on Information and Communication Technologies10.1109/WICT.2012.6409171(733-738)Online publication date: Oct-2012
      • (2012)Parallelizing multiclass support vector machines for scalable image annotationNeural Computing and Applications10.1007/s00521-012-1237-224:2(367-381)Online publication date: 31-Oct-2012
      • (2011)Parallelizing multiclass Support Vector Machines for scalable image annotation2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)10.1109/FSKD.2011.6020073(2691-2694)Online publication date: Jul-2011
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      • (2010)Compacted Dither Pattern Codes versus Principal Component Analysis in video visual depiction2010 International Symposium on Information Technology10.1109/ITSIM.2010.5561298(1-6)Online publication date: Jun-2010
      • (2010)Experiences with shape classification through fuzzy c-means using geometrical and moments descriptorsProceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion10.1007/978-3-642-27169-4_14(189-203)Online publication date: 17-Aug-2010
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