research-article

Incremental probabilistic Latent Semantic Analysis for video retrieval

Authors:

Ruben Fernandez-Beltran,

Filiberto PlaAuthors Info & Claims

Image and Vision Computing, Volume 38, Issue C

Pages 1 - 12

https://doi.org/10.1016/j.imavis.2015.02.003

Published: 01 June 2015 Publication History

Abstract

Recent research trends in Content-based Video Retrieval have shown topic models as an effective tool to deal with the semantic gap challenge. In this scenario, this paper has a dual target: (1) it is aimed at studying how the use of different topic models (pLSA, LDA and FSTM) affects video retrieval performance; (2) a novel incremental topic model (IpLSA) is presented in order to cope with incremental scenarios in an effective and efficient way. A comprehensive comparison among these four topic models using two different retrieval systems and two reference benchmarking video databases is provided. Experiments revealed that pLSA is the best model in sparse conditions, LDA tend to outperform the rest of the models in a dense space and IpLSA is able to work properly in both cases. Display Omitted A study of the use of topic models for video retrieval is presented.A new topic model to deal with incremental retrieval scenarios is proposed.Comparison of four topic models using two different retrieval functionsThe results highlight the performance differences among the topic models.

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Published In

cover image Image and Vision Computing

Image and Vision Computing Volume 38, Issue C

June 2015

74 pages

ISSN:0262-8856

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Butterworth-Heinemann

United States

Publication History

Published: 01 June 2015

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Cited By

Cao SGuo DCao LLi SNie JSingh ALv H(2023)VisDmk: visual analysis of massive emotional danmaku in online videosThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-022-02748-z39:12(6553-6570)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00371-022-02748-z
Chen LChen DLai M(2020)A novel time-shifting method to find popular blog post topicsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-04485-324:13(9705-9725)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1007/s00500-019-04485-3
Chen L(2019)Based on The Document-Link and Time-Clue Relationships Between Blog Posts to Improve the Performance of Google Blog SearchInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.201901010315:1(52-75)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJSWIS.2019010103
Fernandez-Beltran RPla F(2018)Prior-based probabilistic latent semantic analysis for multimedia retrievalMultimedia Tools and Applications10.1007/s11042-017-5247-z77:13(16771-16793)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11042-017-5247-z
Chen L(2017)Finding the Semantic Relationship Between Wikipedia Articles Based on a Useful Entry RelationshipInternational Journal of Data Warehousing and Mining10.4018/IJDWM.201710010313:4(33-52)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.4018/IJDWM.2017100103
Mane PBawane N(2016)An effective technique for the content based image retrieval to reduce the semantic gap based on an optimal classifier techniquePattern Recognition and Image Analysis10.1134/S105466181603015926:3(597-607)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1134/S1054661816030159

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