Article

IGroup: web image search results clustering

Authors:

Wei-Ying MaAuthors Info & Claims

MM '06: Proceedings of the 14th ACM international conference on Multimedia

Pages 377 - 384

https://doi.org/10.1145/1180639.1180720

Published: 23 October 2006 Publication History

Abstract

In this paper, we propose, IGroup, an efficient and effective algorithm that organizes Web image search results into clusters. IGroup is different from all existing Web image search results clustering algorithms that only cluster the top few images using visual or textual features. Our proposed algorithm first identifies several query-related semantic clusters based on a key phrases extraction algorithm originally proposed for clustering general Web search results. Then, all the resulting images are separated and assigned to corresponding clusters. As a result, all the resulting images are organized into a clustering structure with semantic level. To make the best use of the clustering results, a new user interface (UI) is proposed. Different from existing Web image search interfaces, which show only a limited number of suggested query terms or representative image thumbnails of some clusters, the proposed interface displays both representative thumbnails and appropriate titles of semantically coherent image clusters. Comprehensive user studies have been completed to evaluate both the clustering algorithm and the new UI.

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Meng LTan AWunsch II DEzeanya-Esiobu C(2019)Personalized Web Image OrganizationIndigenous Knowledge and Education in Africa10.1007/978-3-030-02985-2_4(93-110)Online publication date: 1-May-2019
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Index Terms

IGroup: web image search results clustering

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Published In

cover image ACM Conferences

MM '06: Proceedings of the 14th ACM international conference on Multimedia

October 2006

1072 pages

ISBN:1595934472

DOI:10.1145/1180639

General Chairs:
Klara Nahrstedt
UIUC
,
Matthew Turk
UCSB
,
Program Chairs:
Yong Rui
Microsoft Research
,
Wolfgang Klas
Universität Wien
,
Ketan Mayer-Patel
UNC

Copyright © 2006 ACM.

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Published: 23 October 2006

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MM06: The 14th ACM International Conference on Multimedia 2006

October 23 - 27, 2006

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

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https://dl.acm.org/doi/10.1007/s11042-018-7131-x
Meng LTan AWunsch II DEzeanya-Esiobu C(2019)Personalized Web Image OrganizationIndigenous Knowledge and Education in Africa10.1007/978-3-030-02985-2_4(93-110)Online publication date: 1-May-2019
https://doi.org/10.1007/978-3-030-02985-2_4
Meng LTan AWunsch II DMeng LTan AWunsch II D(2019)Clustering and Its Extensions in the Social Media DomainAdaptive Resonance Theory in Social Media Data Clustering10.1007/978-3-030-02985-2_2(15-44)Online publication date: 1-May-2019
https://doi.org/10.1007/978-3-030-02985-2_2
Patil SKshirsagar VNagori M(2018)Fetching Relevant Images and Videos in Keyword Based Search Mechanism with Hypergraph Learning and Similarity Matching2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT.2018.8494191(1-6)Online publication date: Jul-2018
https://doi.org/10.1109/ICCCNT.2018.8494191
Singh SKumar P(2018)User specific context construction for personalized multimedia retrievalMultimedia Tools and Applications10.1007/s11042-017-4961-x77:11(13459-13486)Online publication date: 1-Jun-2018
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Panda RRoy-Chowdhury A(2017)Collaborative Summarization of Topic-Related Videos2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2017.455(4274-4283)Online publication date: Jul-2017
https://doi.org/10.1109/CVPR.2017.455
Jiang YWang JWang QLiu WNgo C(2016)Hierarchical Visualization of Video Search Results for Topic-Based BrowsingIEEE Transactions on Multimedia10.1109/TMM.2016.261423318:11(2161-2170)Online publication date: 1-Nov-2016
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Bracamonte THogan APoblete B(2016)Applying Community Detection Methods to Cluster Tags in Multimedia Search Results2016 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2016.0106(467-474)Online publication date: Dec-2016
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Poomagal SVisalakshi PHamsapriya T(2015)A novel method for clustering tweets in TwitterInternational Journal of Web Based Communities10.1504/IJWBC.2015.06854011:2(170-187)Online publication date: 1-Apr-2015
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