Title: A content-based image retrieval using PCA and SOM

Authors: Marouane Ben Haj Ayech; Hamid Amiri

Addresses: National School of Engineering of Tunis, University of Tunis El Manar, Tunis, Tunisia ' National School of Engineering of Tunis, University of Tunis El Manar, Tunis, Tunisia

Abstract: Image search engines have progressed to allow an efficient retrieval. A common trend consists in the construction of a visual vocabulary, in order to apply the BOW model for image indexing. In this paper, we proposed an approach to build an efficient visual vocabulary: First, the feature space composed of SIFT descriptors is transformed into a lower-dimensional space using the Principal Component Analysis (PCA). Second, the resulting feature space is clustered using the Self Organising Map (SOM) and it results in a map of visual words. The proposed model, called PCA-SOM, is evaluated using a dataset of vehicle images from Pascal VOC 2007 benchmark and the experiments show encouraging results.

Keywords: BoW; bag-of-words; CBIR; content-based image retrieval; SOM; self organising maps; PCA; principal component analysis; image search engines; SIFT descriptors; feature space; clustering.

DOI: 10.1504/IJSISE.2016.078259

International Journal of Signal and Imaging Systems Engineering, 2016 Vol.9 No.4/5, pp.276 - 282

Received: 23 May 2015
Accepted: 08 Feb 2016
Published online: 10 Aug 2016 *

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