Authors:
Hanen Karamti
1
;
Hadil Shaiba
2
and
Abeer M. Mahmoud
3
Affiliations:
1
Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, PO Box 84428, Riyadh, Saudi Arabia, MIRACL Laboratory, ISIMS, University of Sfax, B.P. 242, 3021 Sakiet Ezzit, Sfax, Tunisia
;
2
Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, PO Box 84428, Riyadh, Saudi Arabia
;
3
Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, PO Box 84428, Riyadh, Saudi Arabia, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
Keyword(s):
Deep Convolutional Neural Network, Image Retrieval, Fuzzy C-Means Clustering, Feature Extraction.
Abstract:
In the last decennia, several works have been developed to extract global/or local features from images. However, the performance of image retrieval stay surfing from the problem of semantic interpretation of the visual content of images (semantic gap). Recently, deep neural networks (DCNNs) showed excellent performance in different fields like image retrieval for feature extraction compared to traditional techniques. Although, Fuzzy C-Means (FCM) Clustering Algorithm that is a shallow learning method, but it has a competitive performance in the clustering field. In this paper, we present a new method for feature extraction combining DCNN and Fuzzy c-means, where DCNN gives a compact representation of images and FCM clusters the features and enhances the real-time for searching. The proposed method is performed against other methods in literature on two benchmark datasets: Oxford5K and Inria Holidays, where the proposed method overbeats respectively 83% and 86%.