ACM Computing Surveys (CSUR)
Image Databases: Search and Retrieval of Digital Imagery
Image Databases: Search and Retrieval of Digital Imagery
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Generic similarity search engine demonstrated by an image retrieval application
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
CoPhIR Image Collection under the Microscope
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Similarity query postprocessing by ranking
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
Parameter-free and domain-independent similarity search with diversity
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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As the volume of multimedia data available on internet is tremendously increasing, the content-based similarity search becomes a popular approach to multimedia retrieval. The most popular retrieval concept is the k nearest neighbor (kNN) search. For a long time, the kNN queries provided an effective retrieval in multimedia databases. However, as today's multimedia databases available on the web grow to massive volumes, the classic kNN query quickly loses its descriptive power. In this paper, we introduce a new similarity query type, the k distinct nearest neighbors (kDNN), which aims to generalize the classic kNN query to be more robust with respect to the database size. In addition to retrieving just objects similar to the query example, the kDNN further ensures the objects within the result have to be distinct enough, i.e. excluding near duplicates.