Stella Vetova, Ivo Draganov, Ivan Ivanov, Valeri Mladenov, "CBIR Efficiency Enhancement Using Local Features Algorithm with Hausdorff Distance," WSEAS Transactions on Computer Research, vol. 5, pp. 116-123, 2017, DOI:
Stella Vetova, Ivo Draganov, Ivan Ivanov, Valeri Mladenov. CBIR Efficiency Enhancement Using Local Features Algorithm with Hausdorff Distance.
WSEAS Transactions on Computer Research. 2017;5:116-123.
WSEAS Transactions on Computer Research
Print ISSN: 1991-8755, E-ISSN: 2415-1521
Volume 5, 2017
CBIR Efficiency Enhancement Using Local Features Algorithm with Hausdorff Distance
Authors: , , ,
Abstract: The paper below discusses the pros and cons of the local and global features in CBIR. To this end, four CBIR algorithms are designed and studied in terms of effectiveness. Two of them are based on local features extraction and the similarity is computed through Hausdorff distance or Euclidean distance respectively. The rest of the algorithms use global features extraction and the same two similarity distance metrics. For the feature extraction the Dual-Tree Complex Wavelet transform (DT CWT) is applied. The conducted experiments show that the local Features Algorithm with Hausdorff distance (LFAH) which was recently proposed in our previous study demonstrates better results in terms of effectiveness.
Pages: 116-123 WSEAS Transactions on Computer Research, ISSN / E-ISSN: 1991-8755 / 2415-1521, Volume 5, 2017, Art. #14 DOI: *As the DOI is a unique identifier, it is already available in the pdf version.
**The DOI link will be activated in the first midst of January 2026.
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