Visible to the public Image Classification using Convolution Neural Network Based Hash Encoding and Particle Swarm Optimization

TitleImage Classification using Convolution Neural Network Based Hash Encoding and Particle Swarm Optimization
Publication TypeConference Paper
Year of Publication2020
AuthorsAl-Janabi, S. I. Ali, Al-Janabi, S. T. Faraj, Al-Khateeb, B.
Conference Name2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI)
KeywordsClassification algorithms, compositionality, convolution neural network, convolutional neural nets, convolutional neural network, Data models, Deep Learning, feature extraction, file organisation, genetic algorithms, hash algorithms, hash encoding, hashing techniques, high-performance image classifier models, Image analysis, image classification, image retrieval, inception model, Intelligent vehicles, marketing, particle swarm optimisation, particle swarm optimization, Predictive models, pubcrawl, Resiliency, sequential model, Training, transfer values, TRV, TV, visualization
AbstractImage Retrieval (IR) has become one of the main problems facing computer society recently. To increase computing similarities between images, hashing approaches have become the focus of many programmers. Indeed, in the past few years, Deep Learning (DL) has been considered as a backbone for image analysis using Convolutional Neural Networks (CNNs). This paper aims to design and implement a high-performance image classifier that can be used in several applications such as intelligent vehicles, face recognition, marketing, and many others. This work considers experimentation to find the sequential model's best configuration for classifying images. The best performance has been obtained from two layers' architecture; the first layer consists of 128 nodes, and the second layer is composed of 32 nodes, where the accuracy reached up to 0.9012. The proposed classifier has been achieved using CNN and the data extracted from the CIFAR-10 dataset by the inception model, which are called the Transfer Values (TRVs). Indeed, the Particle Swarm Optimization (PSO) algorithm is used to reduce the TRVs. In this respect, the work focus is to reduce the TRVs to obtain high-performance image classifier models. Indeed, the PSO algorithm has been enhanced by using the crossover technique from genetic algorithms. This led to a reduction of the complexity of models in terms of the number of parameters used and the execution time.
Citation Keyal-janabi_image_2020