research-article

Relevance feedback for image retrieval in structured multi-feature spaces

Authors:

Divna Djordjevic,

Ebroul IzquierdoAuthors Info & Claims

MobiMedia '06: Proceedings of the 2nd international conference on Mobile multimedia communications

Article No.: 25, Pages 1 - 5

https://doi.org/10.1145/1374296.1374323

Published: 18 September 2006 Publication History

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Abstract

An approach for content-based image retrieval with relevance feedback based on a structured multi-feature space is proposed. It uses a novel kernel for merging multiple feature subspaces into a complementary space. The kernel exploits nature of the data by assigning appropriate weights for each feature set. The weights are dynamically adapted to user preferences in a relevance feedback scenario.

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Cited By

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Bailer W(2012)Learning Multiple Sequence-Based Kernels for Video Concept DetectionProceedings of the 2012 IEEE International Symposium on Multimedia10.1109/ISM.2012.22(73-77)Online publication date: 10-Dec-2012
https://dl.acm.org/doi/10.1109/ISM.2012.22
Bailer W(2012)Sequence kernels for clustering and visualizing near duplicate video segmentsProceedings of the 18th international conference on Advances in Multimedia Modeling10.1007/978-3-642-27355-1_36(383-394)Online publication date: 4-Jan-2012
https://dl.acm.org/doi/10.1007/978-3-642-27355-1_36
Bailer WBerrani SMessina ADe Sutter RSatoh S(2011)Sequence-based kernels for online concept detection in videoProceedings of the 2011 ACM international workshop on Automated media analysis and production for novel TV services10.1145/2072552.2072554(1-6)Online publication date: 1-Dec-2011
https://dl.acm.org/doi/10.1145/2072552.2072554

Index Terms

Relevance feedback for image retrieval in structured multi-feature spaces
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

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MobiMedia '06: Proceedings of the 2nd international conference on Mobile multimedia communications

September 2006

281 pages

ISBN:1595935177

DOI:10.1145/1374296

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

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Published: 18 September 2006

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Bailer W(2012)Learning Multiple Sequence-Based Kernels for Video Concept DetectionProceedings of the 2012 IEEE International Symposium on Multimedia10.1109/ISM.2012.22(73-77)Online publication date: 10-Dec-2012
https://dl.acm.org/doi/10.1109/ISM.2012.22
Bailer W(2012)Sequence kernels for clustering and visualizing near duplicate video segmentsProceedings of the 18th international conference on Advances in Multimedia Modeling10.1007/978-3-642-27355-1_36(383-394)Online publication date: 4-Jan-2012
https://dl.acm.org/doi/10.1007/978-3-642-27355-1_36
Bailer WBerrani SMessina ADe Sutter RSatoh S(2011)Sequence-based kernels for online concept detection in videoProceedings of the 2011 ACM international workshop on Automated media analysis and production for novel TV services10.1145/2072552.2072554(1-6)Online publication date: 1-Dec-2011
https://dl.acm.org/doi/10.1145/2072552.2072554

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Multi-class relevance feedback content-based image retrieval

Kernel Vector Approximation Files for Relevance Feedback Retrieval in Large Image Databases

Image retrieval based on indexing and relevance feedback

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