Received: 20-07-2020 / Accepted: 02-09-2020
Content-based image retrieval has received great attention in recent years because this method overcomes the disadvantages of the text-based image retrieval that is not affected by the lack of or wrong of the text attached to the image. In addition, deep learning methods such as convolutional neural networks have demonstrated their ability to process large-sized data, especially computer vision and image processing. The aims of this study was develop a content-based image retrieval program and method to reduce image query time using the convolutional neural network (CNN). Also, we combined CNN with a binary hashing method to improve image retrieval time. The experimental results on CIFAR-10 and MNIST data sets showed that combining CNN with the binary hashing method for content-based image retrieval achieved an accuracy of approximately 89% on CIFAR-10,98% on MNIST and significantly improved retrieval time.
