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Exploiting the entire feature space with sparsity for automatic image annotation

Published: 28 November 2011 Publication History

Abstract

The explosive growth of digital images requires effective methods to manage these images. Among various existing methods, automatic image annotation has proved to be an important technique for image management tasks, e.g., image retrieval over large-scale image databases. Automatic image annotation has been widely studied during recent years and a considerable number of approaches have been proposed. However, the performance of these methods is yet to be satisfactory, thus demanding more effort on research of image annotation. In this paper, we propose a novel semi supervised framework built upon feature selection for automatic image annotation. Our method aims to jointly select the most relevant features from all the data points by using a sparsity-based model and exploiting both labeled and unlabeled data to learn the manifold structure. Our framework is able to simultaneously learn a robust classifier for image annotation by selecting the discriminating features related to the semantic concepts. To solve the objective function of our framework, we propose an efficient iterative algorithm. Extensive experiments are performed on different real-world image datasets with the results demonstrating the promising performance of our framework for automatic image annotation.

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cover image ACM Conferences
MM '11: Proceedings of the 19th ACM international conference on Multimedia
November 2011
944 pages
ISBN:9781450306164
DOI:10.1145/2072298
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: 28 November 2011

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Author Tags

  1. image annotation
  2. manifold learning
  3. semi-supervised learning
  4. sparse feature selection

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  • Research-article

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MM '11
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MM '11: ACM Multimedia Conference
November 28 - December 1, 2011
Arizona, Scottsdale, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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  • (2023)Literature Review on Development of Feature Selection and Learning Mechanism for Fuzzy Rule-Based SystemRecent Advances in Computer Science and Communications10.2174/266625581666622082316391316:4Online publication date: May-2023
  • (2023)Semi-Supervised Top-k Feature Selection with a General Optimization Framework2023 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME55011.2023.00057(288-293)Online publication date: Jul-2023
  • (2023)A local spline regression-based framework for semi-supervised sparse feature selectionKnowledge-Based Systems10.1016/j.knosys.2023.110265262:COnline publication date: 28-Feb-2023
  • (2023)SemiACOExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119130214:COnline publication date: 15-Mar-2023
  • (2022)A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature SelectionIEEE Access10.1109/ACCESS.2022.320561810(99595-99632)Online publication date: 2022
  • (2022)Self-expressiveness property-induced structured optimal graph for unsupervised feature selectionNeural Computing and Applications10.1007/s00521-022-07678-434:24(22583-22599)Online publication date: 30-Aug-2022
  • (2019)Extended adaptive Lasso for multi-class and multi-label feature selectionKnowledge-Based Systems10.1016/j.knosys.2019.02.021Online publication date: Feb-2019
  • (2019)Semi-supervised One-Pass Multi-view Learning with Variable Features and ViewsNeural Processing Letters10.1007/s11063-019-10037-5Online publication date: 12-Apr-2019
  • (2018)Semi-supervised Feature Selection Based on Least Square Regression with Redundancy Minimization2018 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2018.8489384(1-8)Online publication date: Jul-2018
  • (2018)Nonlinear sparse feature selection algorithm via low matrix rank constraintMultimedia Tools and Applications10.1007/s11042-018-6909-178:23(33319-33337)Online publication date: 4-Dec-2018
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