Image Retrieval based Convolutional Neural Network

Authors

  • Nuha Mohammed Khassaf Iraqi Commission for Computers and Informatics, Informatics Institute for Postgraduate Studies
  • Shaimaa Hameed Shaker Department of Computer Science, University of Technology.

DOI:

https://doi.org/10.23851/mjs.v31i4.897

Keywords:

CBIR, preprocessing, feature extraction, PCA, MAD, GLCM, Fusion

Abstract

At the present time, everyone is interested in dealing with images in different fields such as geographic maps, medical images, images obtaining by Camera, microscope, telescope, agricultural field photos, paintings, industrial parts drawings, space photos, etc. Content Based Image Retrieval (CBIR) is an efficient retrieval of relevant images from databases based on features extracted from the image. Follow the proposed system for retrieving images related to a query image from a large set of images, based approach to extract the texture features present in the image using statistical methods (PCA, MAD, GLCM, and Fusion) after pre-processing of images. The proposed system was trained using 1D CNN using a dataset Corel10k which widely used for experimental evaluation of CBIR performance the results of proposed system shows that the highest accuracy is 97.5% using Fusion (PCA, MAD), where the accuracy is 95% using MAD, 90% using PCA. The performance result is acceptable compared to previous work.

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Published

2020-12-20

How to Cite

[1]
N. M. Khassaf and S. H. Shaker, “Image Retrieval based Convolutional Neural Network”, MJS, vol. 31, no. 4, pp. 43–54, Dec. 2020.

Issue

Section

Computer Science