PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems

Mohammed Amin Belarbi, Saïd Mahmoudi and Ghalem Belalem
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Mohammed Amin Belarbi: Abdelhamid Ibn Badiss University, Faculty of Exact Science and Computer Science, Mostaganem, Algeria
Saïd Mahmoudi: University of Mons, Faculty of Engineering, Mons, Belgium
Ghalem Belalem: Ahmed Ben Bella University, Faculty of Exact and Applied Science, Oran, Algeria

International Journal of Ambient Computing and Intelligence (IJACI), 2017, vol. 8, issue 4, 45-58

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

Date: 2017
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