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Kernel region approximation blocks for indexing heterogonous databases
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This paper presents a new indexing method for visual features in high dimensional vector space using region approximation approach.
In this paper, we proposed an efficient indexing method, called KRA+-Blocks, for heterogeneous feature vectors in high-dimensional multimedia databases. It ...
This paper presents a new indexing method for visual features in high dimensional vector space using region approximation approach.
We propose in this paper a fast and robust hybrid method for non-linear dimensions reduction of composite image features for indexing in large image database.
In the Kernel Region Approximation Blocks, we address as a whole the issues pre- sented in the introduction and related with CBIR, i.e., image representation, ...
We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis.
Missing: indexing databases.
In general, the precision is accurate to 16 significant digits, but you should review the database documentation to determine the expected approximations.
Kernel region approximation blocks for indexing heterogonous databases. This paper presents a new indexing method for visual features in high dimensional ...
The computing resources in a heterogeneous system usually have different architectures for different use- cases. Obviously, database management systems (DBMS).
Oct 24, 2024 · We propose a novel indexing technique, the RA-Blocks (Region Approximated Blocks), to overcome these limitations and improve the similarity ...