Object detection-based deep autoencoder hashing image retrieval

Abstract

Image Retrieval (IR), which returns similar images from a large image database, has become an important task as multimedia data grows. Existing studies utilize hash code representing the image features generated from the whole image, including redundant semantics from the background. In this study, a novel Object Detection-based Hashing IR (ODH-IR) scheme using You Only Look Once (YOLO) and an autoencoder is presented to ignore clutter in the images. Integration of YOLO and the autoencoder provides the most representative hash code depending on meaningful objects in the images. The autoencoder is exploited to compress the detected object vector to the desired bit length of the hash code. The ODH-IR scheme is validated by comparison with the state of the art through three well-known datasets in terms of precise metrics. The ODH-IR totally has the best 35 metric results over 36 measurements and the best avg. mean rank of 1.03. Moreover, it is observed from the three illustrative IR examples that it retrieves the most relevant semantics. The results demonstrate that the ODH-IR is an impactful scheme thanks to the effective hashing method through object detection using YOLO and the autoencoder.