JISE


  [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] [ 10 ] [ 11 ] [ 12 ] [ 13 ] [ 14 ] [ 15 ] [ 16 ]


Journal of Information Science and Engineering, Vol. 37 No. 1, pp. 185-201


An Analysis of Two Novel and Efficient Deep Learning Models for Fast and Accurate Image Retrieval


SHIKHA BHARDWAJ1, GITANJALI PANDOVE2 AND PAWAN KUMAR DAHIYA2
1Department of Electronics and Communication Engineering
DCRUST, Murthal and UIET, KUK, Haryana, 136119 India

2Department of Electronics and Communication Engineering
DCRUST, Murthal, Haryana, 131039 India
E-mail: shikpank@yahoo.com; pandove_geet@yahoo.co.in; pawan.dahiya@gmail.com


Due to the presence of various limitations in traditional machine learning algorithms, the interest of the researcher community has been shifted towards Deep learning. In this paper, two Deep learning methods namely Deep belief network (DBN) and Stacked autoencoder (SAE) have been analyzed for image retrieval task. But, in order to retrieve images from vast storehouses, more retrieval time is utilized. To solve this issue of retrieval time, two different indexing techniques namely Similarity-based indexing (SBI) and Clusterbased indexing (CBI) have been used. Thus, four models namely DBN-SBI, DBN-CBI, SAE-SBI and SAE-CBI have been developed and tested on two benchmark datasets, which are MIT-Vistex and INRIA-Holidays. Among these models, DBN-SBI obtains the highest results in terms of Precision, Recall and Retrieval time. Average precision of 98.45% and 86.53% with retrieval time of 0.035 seconds and 0.149 seconds has been obtained on Vistex and Holidays dataset respectively which is higher than many state-of-the-art related models.


Keywords: deep belief network, stacked auto-encoder, similarity-based indexing, cluster-based indexing, image retrieval

  Retrieve PDF document (JISE_202101_12.pdf)