Multi-source shared nearest neighbours for multi-modal image clustering
Multimedia Tools and Applications
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Multimedia search engines are often based on multiple decentralized search services, multiple information sources (text search, audio search, visual search, semantic search engines, etc.), multiple data representation and similarity measures. Heterogeneous multiple search results need to be combined and structured efficiently and generically. In this paper, we propose a new multiple search results clustering algorithm based on the Relevant Set Correlation model [1]. As the model is based on shared neighborhood information only, it allows our new technique to process the different information sources as simple oracles returning ranked lists if relevant objects. We experimented the proposed clustering framework on two image datasets with 5 different CBIR techniques as oracles. The results show that our new multi-source scheme performs better than using each source idependently and that it is robust to the presence of noisy oracles. We also show that it performs better than using an early fusion strategy although this fusion approach makes use of more information about the used visual features and similarity measures