Please use this identifier to cite or link to this item:
|Title:||NUS-WIDE: A real-world web image database from National University of Singapore|
|Authors:||Chua, T.-S. |
Training set construction
|Source:||Chua, T.-S.,Tang, J.,Hong, R.,Li, H.,Luo, Z.,Zheng, Y. (2009). NUS-WIDE: A real-world web image database from National University of Singapore. CIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval : 368-375. ScholarBank@NUS Repository. https://doi.org/10.1145/1646396.1646452|
|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. Copyright 2009 ACM.|
|Source Title:||CIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 13, 2017
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.