Content-Based Image Retrieval with Feature Extraction and Rotation Invariance  

作　　者：Nathanael Okoe Larsey Raphael Mawufemor Kofi Ahiaklo-Kuz Joseph Ncube Nathanael Okoe Larsey;Raphael Mawufemor Kofi Ahiaklo-Kuz;Joseph Ncube(School of Information Engineering/Huzhou University, Zhejiang, China)

机构地区：[1]School of Information Engineering/Huzhou University, Zhejiang, China

出　　处：《Journal of Computer and Communications》2022年第4期24-31,共8页电脑和通信（英文）

摘　　要：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.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.

关 键 词：Rotation Invariant CBIR Image Orientation Angle Detection Convolutional Neural Network Deep Learning Real-Time CBIR Information Retrieval 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]