WordNet: a lexical database for English
Communications of the ACM
Texture Features and Learning Similarity
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Information Processing and Management: an International Journal
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Proceedings of the international conference on Multimedia
Content-based tag processing for Internet social images
Multimedia Tools and Applications
Multi-label learning with incomplete class assignments
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
An efficient color representation for image retrieval
IEEE Transactions on Image Processing
Tag Tagging: Towards More Descriptive Keywords of Image Content
IEEE Transactions on Multimedia
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Collaborative image tagging systems, such as Flickr, are very attractive for supporting keyword-based image retrieval, but some social tags of these collaboratively-tagged social images might be imprecise. Some people may use general or high-level words (i.e., abstract tags) to tag their images for saving time and effort, thus such general or high-level tags are too abstract to describe the visual content of social images precisely. As a result, users may not be able to find what they need when they use the specific keywords for query specification. To tackle this problem of abstract tags, a concept ontology is constructed for detecting the abstract tags from large-scale social images. The co-occurrence contexts of social tags and k-NN algorithm with Gaussian Weight are used to find the most specific tags which can signify out the abstract tags. In addition, all the relevant keywords, which are corresponded with intermediate nodes between the high-level concepts (abstract tags) and object classes (most specific tags) on our concept ontology, are added to enrich the lists of social tags, so that users can have more choices to select various keywords for query specification. We have tested our proposed algorithms on two data sets with different images.