Article

A novel log-based relevance feedback technique in content-based image retrieval

Authors:

Michael R. LyuAuthors Info & Claims

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

Pages 24 - 31

https://doi.org/10.1145/1027527.1027533

Published: 10 October 2004 Publication History

Abstract

Relevance feedback has been proposed as an important technique to boost the retrieval performance in content-based image retrieval (CBIR). However, since there exists a semantic gap between low-level features and high-level semantic concepts in CBIR, typical relevance feedback techniques need to perform a lot of rounds of feedback for achieving satisfactory results. These procedures are time-consuming and may make the users bored in the retrieval tasks. For a long-term study purpose in CBIR, we notice that the users' feedback logs can be available and employed for helping the retrieval tasks in CBIR systems. In this paper, we propose a novel scheme to study the log-based relevance feedback (LRF) technique for improving retrieval performance and reducing the semantic gap in CBIR. In order to effectively incorporate the users' feedback logs, we propose a modified support vector machine (SVM) technique called soft label support vector machine (SLSVM) to construct the LRF algorithm in CBIR. We conduct extensive experiments to evaluate the performance of our proposed algorithm. Compared with the typical approach using query expansion (QEX) technique, we demonstrate that our proposed scheme can significantly improve the retrieval performance of semantic image retrieval from detailed experiments.

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Cited By

Cha G(2020)Similarity ranking technique exploiting the structure of similarity relationshipsComputing10.1007/s00607-020-00859-w105:3(559-576)Online publication date: 17-Nov-2020
https://doi.org/10.1007/s00607-020-00859-w
Supreeth HPatil C(2018)Efficient multiple moving object detection and tracking using combined background subtraction and clusteringSignal, Image and Video Processing10.1007/s11760-018-1259-z12:6(1097-1105)Online publication date: 6-Mar-2018
https://doi.org/10.1007/s11760-018-1259-z
Chang E(2018)Semantic Modeling and Knowledge Representation for Multimedia DataEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_1038(3399-3405)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_1038
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Index Terms

A novel log-based relevance feedback technique in content-based image retrieval
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

October 2004

1028 pages

ISBN:1581138938

DOI:10.1145/1027527

General Chairs:
Henning Schulzrinne
Columbia University
,
Nevenka Dimitrova
Philips Research
,
Program Chairs:
Angela Sasse
UCL
,
Sue Moon
KAIST
,
Rainer Lienhart
U Augsburg

Copyright © 2004 ACM.

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Published: 10 October 2004

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MM04: 2004 12th Annual ACM International Conference on Multimedia

October 10 - 16, 2004

NY, New York, USA

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

Cha G(2020)Similarity ranking technique exploiting the structure of similarity relationshipsComputing10.1007/s00607-020-00859-w105:3(559-576)Online publication date: 17-Nov-2020
https://doi.org/10.1007/s00607-020-00859-w
Supreeth HPatil C(2018)Efficient multiple moving object detection and tracking using combined background subtraction and clusteringSignal, Image and Video Processing10.1007/s11760-018-1259-z12:6(1097-1105)Online publication date: 6-Mar-2018
https://doi.org/10.1007/s11760-018-1259-z
Chang E(2018)Semantic Modeling and Knowledge Representation for Multimedia DataEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_1038(3399-3405)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_1038
Pinjarkar LSharma MSelot S(2017)Novel System for Color Logo Recognition Using Optimization and Learning Based Relevance Feedback TechniqueInternational Journal of Computer Vision and Image Processing10.4018/IJCVIP.20171001037:4(28-40)Online publication date: Oct-2017
https://doi.org/10.4018/IJCVIP.2017100103
Chang E(2017)Semantic Modeling and Knowledge Representation for Multimedia DataEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_1038-2(1-6)Online publication date: 13-Jan-2017
https://doi.org/10.1007/978-1-4899-7993-3_1038-2
Zhiqiang ZHua SYun WZou Q(2016)Annotation-retrieval reinforcement by visual cognition modeling on manifoldNeurocomputing10.1016/j.neucom.2015.07.162215:C(150-159)Online publication date: 26-Nov-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.07.162
Jyothi BMadhaveeLatha YKrishna Mohan PReddy V(2016)Region Based Multiple Features for an Effective Content Based Access Medical Image Retrieval an Integrated with Relevance Feedback ApproachSwarm, Evolutionary, and Memetic Computing10.1007/978-3-319-48959-9_16(176-187)Online publication date: 1-Dec-2016
https://doi.org/10.1007/978-3-319-48959-9_16
Mosbah MBoucheham B(2015)Majority Voting Re-ranking Algorithm for Content Based-Image RetrievalMetadata and Semantics Research10.1007/978-3-319-24129-6_11(121-131)Online publication date: 3-Nov-2015
https://doi.org/10.1007/978-3-319-24129-6_11
Bugatti PKaster DPonciano-Silva MTraina CAzevedo-Marques PTraina A(2014)PRoSPerComputers in Biology and Medicine10.1016/j.compbiomed.2013.11.01545(8-19)Online publication date: 1-Feb-2014
https://dl.acm.org/doi/10.1016/j.compbiomed.2013.11.015
Zhang LWang LLin W(2013)A semantic subspace learning method to exploit relevance feedback log data for image retrieval2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)10.1109/CIDM.2013.6597234(178-183)Online publication date: Apr-2013
https://doi.org/10.1109/CIDM.2013.6597234
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