NUS-WIDE

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


References in zbMATH (referenced in 19 articles )

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  1. He, Jia; Du, Changying; Zhuang, Fuzhen; Yin, Xin; He, Qing; Long, Guoping: Online Bayesian max-margin subspace learning for multi-view classification and regression (2020)
  2. Chu, Hong-Min; Huang, Kuan-Hao; Lin, Hsuan-Tien: Dynamic principal projection for cost-sensitive online multi-label classification (2019)
  3. Feng, Songhe; Lang, Congyan: Graph regularized low-rank feature mapping for multi-label learning with application to image annotation (2018)
  4. Francisco Charte, Antonio J. Rivera, David Charte, María J. del Jesus, Francisco Herrera: Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository (2018) arXiv
  5. Liu, Yang; Feng, Lin; Liu, Shenglan; Sun, Muxin: Global similarity preserving hashing (2018)
  6. Yan, Caixia; Luo, Minnan; Liu, Huan; Li, Zhihui; Zheng, Qinghua: Top-(k) multi-class SVM using multiple features (2018)
  7. Antonucci, Alessandro; Corani, Giorgio: The multilabel naive credal classifier (2017)
  8. Chang, Yan-Shuo; Nie, Feiping; Wang, Ming-Yu: Multiview feature analysis via structured sparsity and shared subspace discovery (2017)
  9. Joshi, Shalmali; Ghosh, Joydeep; Reid, Mark; Koyejo, Oluwasanmi: Rényi divergence minimization based co-regularized multiview clustering (2016)
  10. Hamid Amiri, S.; Jamzad, Mansour: Efficient multi-modal fusion on supergraph for scalable image annotation (2015)
  11. Gong, Yunchao; Ke, Qifa; Isard, Michael; Lazebnik, Svetlana: A multi-view embedding space for modeling Internet images, tags, and their semantics (2014) ioport
  12. Kapoor, Ashish; Caicedo, Juan C.; Lischinski, Dani; Kang, Sing Bing: Collaborative personalization of image enhancement (2014) ioport
  13. Liu, Xianglong; He, Junfeng; Lang, Bo: Multiple feature kernel hashing for large-scale visual search (2014)
  14. Xie, Haoran; Li, Qing; Mao, Xudong; Li, Xiaodong; Cai, Yi; Rao, Yanghui: Community-aware user profile enrichment in folksonomy (2014) ioport
  15. Hou, Jian; Liu, Wei-Xue: On building a universal and compact visual vocabulary (2013) ioport
  16. Li, Zechao; Liu, Jing; Xu, Changsheng; Lu, Hanqing: MLRank: multi-correlation learning to rank for image annotation (2013)
  17. Yu, Zhiwen; Wong, Hau-San; You, Jane; Han, Guoqiang: Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme (2012) ioport
  18. Bao, Bing-Kun; Ni, Bingbing; Mu, Yadong; Yan, Shuicheng: Efficient region-aware large graph construction towards scalable multi-label propagation (2011) ioport
  19. Zhuang, Yueting; Han, Yahong; Wu, Fei; Yang, Jiacheng: Stable multi-label boosting for image annotation with structural feature selection (2011)