Author(s)

Nathanael Okoe Larsey, Raphael Mawufemor Kofi Ahiaklo-Kuz^*, Joseph Ncube

School of Information Engineering/Huzhou University, Zhejiang, China.

Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered orientation, training a CBIR system to detect and correct the angle is complex. While it is possible to construct rotation-invariant features by hand, retrieval accuracy will be low because hand engineering only creates low-level features, while deep learning methods build high-level and low-level features simultaneously. This paper presents a novel approach that combines a deep learning orientation angle detection model with the CBIR feature extraction model to correct the rotation angle of any image. This offers a unique construction of a rotation-invariant CBIR system that handles the CNN features that are not rotation invariant. This research also proposes a further study on how a rotation-invariant deep CBIR can recover images from the dataset in real-time. The final results of this system show significant improvement as compared to a default CNN feature extraction model without the OAD.

Rotation Invariant CBIR, Image Orientation Angle Detection, Convolutional Neural Network, Deep Learning, Real-Time CBIR, Information Retrieval

Larsey, N. , Ahiaklo-Kuz, R. and Ncube, J. (2022) Content-Based Image Retrieval with Feature Extraction and Rotation Invariance. Journal of Computer and Communications, 10, 24-31. doi: 10.4236/jcc.2022.104003.