Comparison of feature-based and image registration-based retrieval of image data using multidimensional data access methods

Date

2013-07-01

Author

Arslan, Serdar
Yazıcı, Adnan
Sacan, Ahmet
Toroslu, İsmail Hakkı
Acar, Esra

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

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In information retrieval, efficient similarity search in 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 trade-offs for each of these methods are demonstrated on three different image data sets. Similarity of images is obtained either by a feature-based similarity measure using four MPEG-7 low-level descriptors or by a whole image-based similarity measure. The effect of these similarity measurement techniques on the retrieval process is also evaluated through the performance tests performed on several data sets. We show that using low-level features of images in the similarity measurement function results in significantly better accuracy and time performance compared to the whole-image based approach. Moreover, an optimization of feature contributions to the distance measure for feature-based approach can identify the most relevant features and is necessary to obtain maximum accuracy. We further show that multidimensional scaling can achieve comparable accuracy, while speeding-up the query times significantly by allowing the use of spatial access methods.

Subject Keywords

Information Systems and Management

URI

https://hdl.handle.net/11511/41979

Journal

DATA & KNOWLEDGE ENGINEERING

DOI

https://doi.org/10.1016/j.datak.2013.01.007

Collections

Department of Computer Engineering, Article

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Citation Formats

S. Arslan, A. Yazıcı, A. Sacan, İ. H. Toroslu, and E. Acar, “Comparison of feature-based and image registration-based retrieval of image data using multidimensional data access methods,” DATA & KNOWLEDGE ENGINEERING, pp. 124–145, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41979.