2014
DOI: 10.1186/1687-5281-2014-22
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Multiscale texture retrieval based on low-dimensional and rotation-invariant features of curvelet transform

Abstract: Multiscale-based texture retrieval algorithms use low-dimensional feature sets in general. However, they do not have as good retrieval performances as those of the state-of-the-art techniques in the literature. The main motivation of this study is to use low-dimensional multiscale features to provide comparable retrieval performances with the state-of-the-art techniques. The proposed features of this study are low-dimensional, robust against rotation, and have better performance than the earlier multiresolutio… Show more

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Cited by 5 publications
(1 citation statement)
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References 28 publications
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“…However, the traditional text-based techniques require manual labelling which is time-consuming and very subjective. On the other hand, CBIR techniques [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] retrieve images entirely based on their low-level features such as texture, colour and shape.…”
Section: Introductionmentioning
confidence: 99%
“…However, the traditional text-based techniques require manual labelling which is time-consuming and very subjective. On the other hand, CBIR techniques [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] retrieve images entirely based on their low-level features such as texture, colour and shape.…”
Section: Introductionmentioning
confidence: 99%