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HyperSSR: A hypergraph based semi-supervised ranking method for visual search reranking
- Source :
- Neurocomputing. 274:50-57
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- Recently, considerable efforts have been made in visual search reranking towards refining initial text-based image search results. In this paper, we propose a hypergraph based semi-supervised ranking method called HyperSSR for image search reranking. According to the basic visual consistency principle that visually similar images should have similar ranking scores, we introduce the hypergraph to capture the intrinsic geometrical structure of the data distribution. To build a robust hypergraph, a novel hypergraph construction approach is developed to incorporate relevance and pseudo relevance degree information from labeled and unlabeled samples, respectively. Based on the premise that a ranking model should work better with the prior pairwise preferences, we jointly incorporate the hypergraph regularizer and the prior pairwise preferences information into a unified ranking learning framework. Experimental results on MSRA-MM 1.0 dataset suggest our proposed approach produces superior performances compared with several state-of-the-art methods.
- Subjects :
- Visual search
Hypergraph
business.industry
Cognitive Neuroscience
Pattern recognition
02 engineering and technology
Machine learning
computer.software_genre
Computer Science Applications
Image (mathematics)
Ranking (information retrieval)
Consistency (database systems)
Artificial Intelligence
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Relevance (information retrieval)
Pairwise comparison
Artificial intelligence
business
computer
Image retrieval
Mathematics
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 274
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........c1cc09e91c1e21b752e8bcf92b8cb77b