Comparison of multidimensional data access methods for feature-based image retrieval

Date

2010-11-18

Author

Arslan, Serdar
Saçan, Ahmet
Açar, Esra
Toroslu, İsmail Hakkı
Yazıcı, Adnan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Within the scope of information retrieval, efficient similarity search in large document or multimedia collections is a critical task. In this paper, we present a rigorous comparison of three different approaches to the image retrieval problem, including cluster-based indexing, distance-based indexing, and multidimensional scaling methods. The time and accuracy tradeoffs for each of these methods are demonstrated on a large Corel image database. Similarity of images is obtained via a featurebased similarity measure using four MPEG-7 low-level descriptors. We show that an optimization of feature contributions to the distance measure can identify irrelevant features and is necessary to obtain the maximum accuracy. We further show that using multidimensional scaling can achieve comparable accuracy, while speeding-up the query times significantly by allowing the use of spatial access methods. © 2010 IEEE.

Subject Keywords

Multidimensional Access Methods, MPEG-7, Fastmap, LMDS , SlimTree , BitMatrix, Indexing, CBIR

URI

https://hdl.handle.net/11511/56740

DOI

https://doi.org/10.1109/icpr.2010.797

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

S. Arslan, A. Saçan, E. Açar, İ. H. Toroslu, and A. Yazıcı, “Comparison of multidimensional data access methods for feature-based image retrieval,” 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56740.