Content Based Image Retrieval

Abstract

Abstract Multimedia became a primary aspect for all different social and business categories. This day, societies figure out using images, videos, and sounds. Productions, marketing, and people's communication depend on social media using texting and multimedia. Servers hold huge datasets according to the huge numbers of users and their media. The search became a daily manner. Search by text is very easy for the labeled datasets but a search by image is very useful for unlabeled seen. Last few days search by image became a trend for many search engines. Developers built different programs to help users find locations, shops, people, and objects using a single image. This technology is called content-based image retrieval (CBIR). Content refers to the whole image or part of the image. Unfortunately, there is no accurate retrieval accuracy over the huge image datasets. Researchers find in the CBIR topic hot challenging points to propose more accurate models using intelligent methods with the lowest complexity. In this thesis, we proposed recent approaches using machine learning and deep learning to classify images into categories and retrieve relevant images to the input image from different sizes of datasets. Image classification is a part of computer vision and it splits the datasets into categories to make the retrieve operation easier.. In this study, we utilize various types of algorithms such as; supervised, unsupervised and deep learning. Convolution neural network (CNN) is applied as a main deep learning model. We proposed a model using CNN, model fused CNN with recurrent neural networks like LSTM and GRU, and model fused it with traditional algorithms like Decision tree (DT) and Support Vector Machine (SVM). Also, we used a variety of datasets in sizes and types. The datasets is an important factor to measure the evaluation of the CBIR system, so we used different datasets in types and size. There is many images type like; people, objects, medical and digital numbers.