Article

Hierarchical clustering of WWW image search results using visual, textual and link information

Authors:

Ji-Rong WenAuthors Info & Claims

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

Pages 952 - 959

https://doi.org/10.1145/1027527.1027747

Published: 10 October 2004 Publication History

Abstract

We consider the problem of clustering Web image search results. Generally, the image search results returned by an image search engine contain multiple topics. Organizing the results into different semantic clusters facilitates users' browsing. In this paper, we propose a hierarchical clustering method using visual, textual and link analysis. By using a vision-based page segmentation algorithm, a web page is partitioned into blocks, and the textual and link information of an image can be accurately extracted from the block containing that image. By using block-level link analysis techniques, an image graph can be constructed. We then apply spectral techniques to find a Euclidean embedding of the images which respects the graph structure. Thus for each image, we have three kinds of representations, i.e. visual feature based representation, textual feature based representation and graph based representation. Using spectral clustering techniques, we can cluster the search results into different semantic clusters. An image search example illustrates the potential of these techniques.

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Cited By

Meena PKumar HYadav S(2024)A Volumetric Saliency Guided Image Summarization for RGB-D Indoor Scene ClassificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.341294934:11(10917-10929)Online publication date: Nov-2024
https://doi.org/10.1109/TCSVT.2024.3412949
Knittel MSpringer MDickerson JHajiaghayi MKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Generalized reductionsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619116(17218-17242)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619116
Huynh MLe QNguyen VNguyen T(2023)Web Page Segmentation: A DOM-Structural Cohesion Analysis ApproachWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_25(319-333)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-981-99-7254-8_25
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Index Terms

Hierarchical clustering of WWW image search results using visual, textual and link information
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining
      1. Clustering

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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.

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 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 2,145 of 8,556 submissions, 25%

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https://doi.org/10.1109/TCSVT.2024.3412949
Knittel MSpringer MDickerson JHajiaghayi MKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Generalized reductionsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619116(17218-17242)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619116
Huynh MLe QNguyen VNguyen T(2023)Web Page Segmentation: A DOM-Structural Cohesion Analysis ApproachWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_25(319-333)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-981-99-7254-8_25
Jayashree SDias GAndrew JSaha SMaurel FFerrari S(2022)Multimodal Web Page Segmentation Using Self-organized Multi-objective ClusteringACM Transactions on Information Systems10.1145/348096640:3(1-49)Online publication date: 7-Mar-2022
https://dl.acm.org/doi/10.1145/3480966
Skluzacek TChard KFoster I(2022)Automated metadata extraction: challenges and opportunities2022 IEEE 18th International Conference on e-Science (e-Science)10.1109/eScience55777.2022.00088(495-500)Online publication date: Oct-2022
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Tekli J(2022)An overview of cluster-based image search result organization: background, techniques, and ongoing challengesKnowledge and Information Systems10.1007/s10115-021-01650-9Online publication date: 11-Feb-2022
https://doi.org/10.1007/s10115-021-01650-9
Sharma DSingh ASharma SSrivastava GLin J(2022)Task-specific image summaries using semantic information and self-supervisionSoft Computing10.1007/s00500-021-06603-626:16(7581-7594)Online publication date: 21-Jan-2022
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Petridis SShin HChilton L(2021)SymbolFinder: Brainstorming Diverse Symbols Using Local Semantic NetworksThe 34th Annual ACM Symposium on User Interface Software and Technology10.1145/3472749.3474757(385-399)Online publication date: 10-Oct-2021
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Yan DWang YWang JWu GWang H(2021)Fast Communication-Efficient Spectral Clustering over Distributed DataIEEE Transactions on Big Data10.1109/TBDATA.2019.29079857:1(158-168)Online publication date: 1-Mar-2021
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