Distinct nearest neighbors queries for similarity search in very large multimedia databases

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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Retrieval models Ge...

• Type: Conference
• Year: 2009
• Where: WIDM
• Authors: Tomás Skopal, Vlastislav Dohnal, Michal Batko, Pavel Zezula
• Keywords: Classic KNN Query, Internet Technology, KNN Query, Similarity Search, WIDM 2009