MFI-tree: An effective multi-feature index structure for weighted query application | Zendy

Yunfeng He | Zendy; Junqing Yu | Zendy

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MFI-tree: An effective multi-feature index structure for weighted query application

Author(s) -

Yunfeng He,

Junqing Yu

Publication year - 2010

Publication title -

computer science and information systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.244

H-Index - 24

eISSN - 2406-1018

pISSN - 1820-0214

DOI - 10.2298/csis1001139h

Subject(s) - computer science , search engine indexing , tree (set theory) , feature (linguistics) , index (typography) , data mining , pattern recognition (psychology) , tree structure , artificial intelligence , r tree , feature vector , database index , search tree , search algorithm , binary tree , spatial database , algorithm , spatial analysis , mathematics , mathematical analysis , linguistics , philosophy , statistics , world wide web

Multi-Feature Index Tree (MFI-Tree), a new indexing structure, is proposed to index multiple high-dimensional features of video data for video retrieval through example. MFI-Tree employs tree structure which is beneficial for the browsing application, and retrieves the last level cluster nodes in retrieval application to improve the performance. Aggressive Decided Distance for kNN (ADD-kNN) search algorithm is designed because it can effectively reduce the distance to prune the search space. Experimental results demonstrate that the MFITree and ADD-kNN algorithm have the advantages over sequential scan in performance.

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