Image Classification using Convolution Neural Network Based Hash Encoding and Particle Swarm Optimization

Abstract

Analysis of image contents has become one of the important subjects in modern life. In order to recognize the images with efficient way, several techniques have appeared and periodically enhanced by the developers. Image retrieval becomes one of the main problems that face the computer society inside the revolution of technology. To increase the effectiveness of computing similarities between images, hashing approaches became the focusing of the programmers. Indeed, deep learning in the past few years has been considered the backbone of image analysis using a convolutional neural network (CNN). The paper is providing a survey of the latest work carried out in the field of image retrieval. Several techniques have appeared in this field. However, the most common of these techniques are using neural network-based hash encoding, which can be categorized into three main classes: Supervised, unsupervised, and semi-supervised techniques according to each technique's learning method. The most important related works appeared in the literature are reviewed and constructive comparisons have been done to show the strengths and limitations of various techniques.