Image Retrieval Using Convolutional Autoencoder, InfoGAN, and Vision Transformer Unsupervised Models

Query by Image Content (QBIC), subsequently known as Content-Based Image Retrieval (CBIR), offers an advantageous solution in a variety of applications, including medical, meteorological, search by image, and other applications. Such CBIR systems primarily use similarity matching algorithms to compare image content to get matched images from datasets. They essentially measure the spatial distance between extracted visual features from a query image and its similar versions in the dataset. One of the most challenging query retrieval problems is Facial Sketched-Real Image Retrieval (FSRIR), which is based on content similarity matching. These facial retrieval systems are employed in a variety of contexts, including criminal justice. The difficulties of retrieving such sorts come from the composition of the human face and its distinctive parts. In addition, the comparison between these types of images is made within two different domains. Besides, to our knowledge, there is a few large-scale facial datasets that can be used to assess the performance of the retrieval systems. The success of the retrieval process is governed by the method used to estimate similarity and the efficient representation of compared images. However, by effectively representing visual features, the main challenge-posing component of such systems might be resolved. Hence, this paper has several contributions that fill the research gap in content-based similarity matching and retrieval. The first contribution is extending the Chinese University Face Sketch (CUFS) dataset by including augmented images, introducing to the community a novel dataset named Extended Sketched-Real Image Retrieval (ESRIR). The CUFS dataset has been extended from 100 images to include 53,000 facial sketches and 53,000 real facial images. The paper second contribution is presenting three new systems for sketched-real image retrieval based on convolutional autoencoder, InfoGAN, and Vision Transformer (ViT) unsupervised models for large datasets. Furthermore, to meet the subjective demands of the users due to the prevalence of multiple query formats, the third contribution of the paper is to train and assess the performance of the proposed models on two additional facial datasets of different image types. Recently, the majority of people have preferred searching for brand logo images, but it may be tricky to separate certain brand logo features their alternatives and even from other features in an image. Thus, the fourth contribution is to compare logo image retrieval performance based on visual features derived from each of the three suggested retrieval systems. The paper also presents cloud-based energy and computational complexity saving approaches on large-scale datasets. Due to the ubiquity of touchscreen devices, users often make drawings based on their fantasies for certain object image searches. Thus, the proposed models are tested and assessed on a tough dataset of doodle-scratched human artworks. They are also studied on a multi-category dataset to cover practically all possible image types and situations. The results are compared with those of the most recent algorithms found in the literature. The results show that the proposed systems outperform the recent counterparts.