Comparison of Multidimensional Data Access Methods for Feature-Based Image Retrieval

Arslan, Serdar; Saçan, Ahmet; Acar, Esra; Toroslu, İsmail Hakkı; Yazıcı, Adnan

doi:10.1109/icpr.2010.797

Cited by 3 publications

(4 citation statements)

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“…The most widely used multi-dimensional indexing structures realized from literatures are the R-tree, R*-tree and X -tree and have been seen to perform reasonably well with data chunking [1], [4], [8], [17], [31]- [34], [40], [42]. This technique partition multi-dimensional data set into a coarse-grained hyper-rectangular block to help build an indexing structure that does not suffer much from dead space [31], [40].…”

Section: The Indexing Technique Employs Data Construction Methodsmentioning

confidence: 99%

“…1 [5], [6], [12], [15], [38], and [40], but identifying the most efficient indexing structures for organizing uncertain data with high dimensionality remains an issue. Most of the research works [4], [17], [22], [24], [26]- [30], [33], [34], [39] that utilized indexing structures for organizing data are subjective in their choices and usage of these indexing structures. For instance, in the works of [22] and [34], their choices of R-tree and R*-tree to organize uncertain data and points respectively were based on the simplicity and popularity of the indexing structure.…”

Section: The Indexing Technique Employs Data Construction Methodsmentioning

confidence: 99%

“…The indexing structures were implemented using Java (jdk-8u181-windows-x64) programming language running on a 64bit Windows 7 operating. Each page size is set to 4K byte for all the implemented algorithms, as implemented by previous works [4], [22]. Our experiments were carried out on an Intel core i5 PC with 3.20GHz processor and 16GB main memory.…”

Section: B Experimental Setupmentioning

confidence: 99%

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Analyses of Indexing Techniques on Uncertain Data With High Dimensionality

et al. 2020

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Deploying a solution for handling critical decision-based problem efficiently requires the processing of high-dimensional data. Over the years, due to modern technological advancement, unprecedented volume of uncertain data is been captured and this has necessitated the need to organize such data for better data access performance. To this effect, the use of indexing technique for supporting, organizing, and storing of uncertain data with high dimensionality has become pertinent. However, the choice of an indexing technique to improve search performance is highly influenced by the properties of the underlying data set, data construction methods employed by the indexing structure, and the query types it supports. This paper is motivated to conduct an extensive performance analysis among existing indexing techniques, namely: R-tree, R*-tree and X -tree, in order to realize the most efficient indexing structure for organizing, storing and ultimately improving search performance over uncertain data with high dimensionality. The results of the analyses with regard to CPU processing time and number of nodes visited clearly show the superiority of X -tree over R-tree and R*-tree, as its superiority holds for different data set sizes, data distributions, number of dimensions and even with varying selectivity ratio.INDEX TERMS Data partitioning, indexing techniques, MBR, uncertain data, high-dimensional data.

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Section: The Indexing Technique Employs Data Construction Methodsmentioning

confidence: 99%

Section: The Indexing Technique Employs Data Construction Methodsmentioning

confidence: 99%

Section: B Experimental Setupmentioning

confidence: 99%

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Analyses of Indexing Techniques on Uncertain Data With High Dimensionality

et al. 2020

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“…References [6,7] developed a local auxiliary color maximum vector pattern (LACMVP) which includes stimulating the color and surface data by taking a primary (red, green, and blue) and a secondary channel (value) from two diverse shading spaces (RGB and HSV). A vector design including the size, sign, and position are determined for the most extreme nearby contrast between the middle pixel and its neighbor from the secondary channel.…”

Section: Related Workmentioning

confidence: 99%

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Karthikeyan¹,

Kavida²,

Suresh³

2022

Computer Systems Science and Engineering

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Digitization has created an abundance of new information sources by altering how pictures are captured. Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing. This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets. Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional, and c) noisy and less accurate extraction of image information (semantic and predicted attributes). This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively. Thus, retrieval becomes straightforward and rapid. This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost. We focus on the schema design on a non-clustered index solution, especially cover queries. This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing. Finally, we include non-key columns in addition to the key columns. Experiments on two image data sets 'with and without' filtered indexing show low query cost. We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing. The results show that retrieval by using deep root indexing is simple and fast.

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