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PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems

Author

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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)

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

Suggested Citation

  • Mohammed Amin Belarbi & Saïd Mahmoudi & Ghalem Belalem, 2017. "PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 8(4), pages 45-58, October.
    • Handle: RePEc:igg:jaci00:v:8:y:2017:i:4:p:45-58
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    Cited by:

    1. Abdul Majeed, 2019. "Improving Time Complexity and Accuracy of the Machine Learning Algorithms Through Selection of Highly Weighted Top k Features from Complex Datasets," Annals of Data Science, Springer, vol. 6(4), pages 599-621, December.
    2. Sandipan Sahu & Raghvendra Kumar & Pathan MohdShafi & Jana Shafi & SeongKi Kim & Muhammad Fazal Ijaz, 2022. "A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews," Mathematics, MDPI, vol. 10(9), pages 1-22, May.

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