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%.