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PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems

Mohammed Amin Belarbi, Saïd Mahmoudi, Ghalem Belalem

Source Title: International Journal of Ambient Computing and Intelligence (IJACI)8(4)

ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522523024|DOI: 10.4018/IJACI.2017100104

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MLA

Belarbi, Mohammed Amin, et al. "PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems." IJACI vol.8, no.4 2017: pp.45-58. http://doi.org/10.4018/IJACI.2017100104

APA

Belarbi, M. A., Mahmoudi, S., & Belalem, G. (2017). PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems. International Journal of Ambient Computing and Intelligence (IJACI), 8(4), 45-58. http://doi.org/10.4018/IJACI.2017100104

Chicago

Belarbi, Mohammed Amin, Saïd Mahmoudi, and Ghalem Belalem. "PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems," International Journal of Ambient Computing and Intelligence (IJACI) 8, no.4: 45-58. http://doi.org/10.4018/IJACI.2017100104

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Abstract

Dimensionality reduction in large-scale image research plays an important role for their performance in different applications. In this paper, we explore Principal Component Analysis (PCA) as a dimensionality reduction method. For this purpose, first, the Scale Invariant Feature Transform (SIFT) features and Speeded Up Robust Features (SURF) are extracted as image features. Second, the PCA is applied to reduce the dimensions of SIFT and SURF feature descriptors. By comparing multiple sets of experimental data with different image databases, we have concluded that PCA with a reduction in the range, can effectively reduce the computational cost of image features, and maintain the high retrieval performance as well

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PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems

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