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

Metadata

Show full item record

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

Item Usage Stats

135
views

0
downloads

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

Suggestions

OpenMETU
Core

Implementation of X-Tree with 3D Spatial Index and Fuzzy Secondary Index Keskin, Sinan; Yazıcı, Adnan; Oğuztüzün, Mehmet Halit S. (2011-10-28) In spatial databases, traditional approach is to build separate indexing structures for spatial and non-spatial attributes. This article introduces a new coupled approach that combines a 3D spatial primary index and a fuzzy non-spatial secondary index. Based on tests with several types of queries on a meteorological data set, it is shown that our coupled structure reduces the number of iterations and the time consumed for querying compared with the traditional uncoupled one.
Clustering scientific literature using sparse citation graph analysis Bolelli, Levent; Ertekin Bolelli, Şeyda; Giles, C. Lee (2006-01-01) It is well known that connectivity analysis of linked documents provides significant information about the structure of the document space for unsupervised learning tasks. However, the ability to identify distinct clusters of documents based on link graph analysis is proportional to the density of the graph and depends on the availability of the linking and/or linked documents in the collection. In this paper, we present an information theoretic approach towards measuring the significance of individual word...
Comparison of feature-based and image registration-based retrieval of image data using multidimensional data access methods Arslan, Serdar; Yazıcı, Adnan; Sacan, Ahmet; Toroslu, İsmail Hakkı; Acar, Esra (Elsevier BV, 2013-07-01) 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-...
Implementation of x-tree with 3d spatial index and fuzzy secondary index Keskin, Sinan; Yazıcı, Adnan; Oğuztüzün, Mehmet Halit S.; Department of Computer Engineering (2010) Multidimensional datasets are getting more extensively used in Geographic Information Systems (GIS) applications in recent years. Due to large volume of these datasets efficient querying becomes a significant problem. For this purpose, before creating index structure with these enormous datasets, choosing an efficient index structure is an urgent necessity. The aim of this thesis is to develop an efficient, flexible and extendible index structure which comprises 3D spatial data in primary index and fuzzy at...
On Fuzzy Extensions to Energy Ontologies for Text Processing Applications Kucuk, Dilek; Kucuk, Dogan; Yazıcı, Adnan (2014-10-28) Ubiquitous application areas of domain ontologies include text processing applications like categorizing related documents of the domain, extraction of information from these documents, and semantic search. In this paper, we focus on the utilization of two energy ontologies, one for electrical power quality and the second for wind energy, within such applications. For this purpose, we present fuzzy extensions to these domain ontologies as fuzziness is an essential feature of the ultimate forms of the ontolo...

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.