Cultural Relic Image Retrieval Method Based on Features of SIFT

Wen, Chao; Geng, Guo Hua; Zhu, Xin

doi:10.1109/iccis.2011.119

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2012

2024

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Cited by 3 publications

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“…Finally, similarity measure is presented for the new color feature. Aiming at the cultural relic image retrieval problems in digital archaeology museum of Northwest University, Chao Wen et al [5] proposed an image retrieval method based on Scale Invariant Feature Transform features. Firstly, Scale Invariant Feature Transform features of cultural relic images are extracted and Principal Component Analysis method is adopted to reduce the dimensionality, then an approximate nearest neighbor search algorithm is employed in Scale Invariant Feature Transform feature points matching.…”

Section: Introductionmentioning

confidence: 99%

Improved Image Retrieval Performance Based on Fuzzy C-means Clustering Algorithm

Huting¹,

Yong²

2012

IJACT

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Most traditional and common methods of image retrieval utilize method of adding metadata, for example, captioning, keywords, or descriptions to the images, this method makes retrieval can be performed over the annotation, this paper proposes improved image retrieval performance based on fuzzy c-means algorithm. The segmented regions are indexed by the averaged features in the regions; some regions are extracted to be the features to match the related search results. The experiment shows that the proposed algorithm is effective.

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Section: Introductionmentioning

confidence: 99%