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Tag Relevance Fusion for Social Image Retrieval
- Publication Year :
- 2014
- Publisher :
- arXiv, 2014.
-
Abstract
- Due to the subjective nature of social tagging, measuring the relevance of social tags with respect to the visual content is crucial for retrieving the increasing amounts of social-networked images. Witnessing the limit of a single measurement of tag relevance, we introduce in this paper tag relevance fusion as an extension to methods for tag relevance estimation. We present a systematic study, covering tag relevance fusion in early and late stages, and in supervised and unsupervised settings. Experiments on a large present-day benchmark set show that tag relevance fusion leads to better image retrieval. Moreover, unsupervised tag relevance fusion is found to be practically as effective as supervised tag relevance fusion, but without the need of any training efforts. This finding suggests the potential of tag relevance fusion for real-world deployment.
- Subjects :
- FOS: Computer and information sciences
Fusion
Information retrieval
Computer Networks and Communications
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Single measurement
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Computer Science - Information Retrieval
Set (abstract data type)
Social image
H.3.3
Hardware and Architecture
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Media Technology
Benchmark (computing)
020201 artificial intelligence & image processing
Relevance (information retrieval)
Image retrieval
Software
Information Retrieval (cs.IR)
Information Systems
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....5be517be2352b374110f99bb26080195
- Full Text :
- https://doi.org/10.48550/arxiv.1410.3462