Title
Comparison of feature-based and image registration-based retrieval of image data using multidimensional data access methods.
Abstract
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.
Year
DOI
Venue
2013
10.1016/j.datak.2013.01.007
Data & Knowledge Engineering
Keywords
DocType
Volume
Access methods,Information retrieval,Filtering,Classification,Summarization and visualization,Indexing methods,Content-based image retrieval,Landmark-based multidimensional scaling,OWA,Multimedia indexing,BitMatrix,SlimTree
Journal
86
Issue
ISSN
Citations 
1
0169-023X
3
PageRank 
References 
Authors
0.40
54
5
Name
Order
Citations
PageRank
Serdar Arslan131.07
Adnan Yazici264956.29
Ahmet Sacan39813.19
Ismail Hakki Toroslu4456102.80
Esra Acar5567.10