poster

NUS-WIDE: a real-world web image database from National University of Singapore

Authors:

Yantao ZhengAuthors Info & Claims

CIVR '09: Proceedings of the ACM International Conference on Image and Video Retrieval

Article No.: 48, Pages 1 - 9

https://doi.org/10.1145/1646396.1646452

Published: 08 July 2009 Publication History

Abstract

This paper introduces a web image dataset created by NUS's Lab for Media Search. The dataset includes: (1) 269,648 images and the associated tags from Flickr, with a total of 5,018 unique tags; (2) six types of low-level features extracted from these images, including 64-D color histogram, 144-D color correlogram, 73-D edge direction histogram, 128-D wavelet texture, 225-D block-wise color moments extracted over 5x5 fixed grid partitions, and 500-D bag of words based on SIFT descriptions; and (3) ground-truth for 81 concepts that can be used for evaluation. Based on this dataset, we highlight characteristics of Web image collections and identify four research issues on web image annotation and retrieval. We also provide the baseline results for web image annotation by learning from the tags using the traditional k-NN algorithm. The benchmark results indicate that it is possible to learn effective models from sufficiently large image dataset to facilitate general image retrieval.

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Index Terms

NUS-WIDE: a real-world web image database from National University of Singapore
1. Information systems
  1. Information retrieval
    1. Document representation
  2. Information systems applications

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Published In

cover image ACM Conferences

CIVR '09: Proceedings of the ACM International Conference on Image and Video Retrieval

July 2009

383 pages

ISBN:9781605584805

DOI:10.1145/1646396

Conference Chairs:
Yiannis Kompatsiaris
CERTH-ITI, Greece
,
Stephane Marchand-Maillet
Univ. of Geneva, Switzerland
,
Program Chairs:
Yannis Avrithis
NTUA, Greece
,
Noel O Connor
DCU, Ireland
,
Daniel Gatica-Perez
Idiap Research Institute, Switzerland
,
Tat-Seng Chua
National University of Singapore, Singapore

Copyright © 2009 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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SIGMM: ACM Special Interest Group on Multimedia

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 08 July 2009

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CIVR '09

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SIGMM

CIVR '09: CIVR '09 - International Conference on Image and Video Retrieval

July 8 - 10, 2009

Santorini, Fira, Greece

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https://doi.org/10.1109/TPAMI.2024.3467130
Bian JXie XWang CYang LLai JNie F(2025)Angular Reconstructive Discrete Embedding With Fusion Similarity for Multi-View ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348790737:1(45-59)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3487907
Jiang ZWeng ZLi RZhuang HLin Z(2025)Online weighted hashing for cross-modal retrievalPattern Recognition10.1016/j.patcog.2024.111232161(111232)Online publication date: May-2025
https://doi.org/10.1016/j.patcog.2024.111232
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