Rotation Invariant Deep CBIR
This addresses the challenge of rotation sensitivity in CBIR systems for image retrieval applications, though it appears incremental as it combines existing components.
The paper tackles the problem of building a rotation-invariant content-based image retrieval (CBIR) system by introducing a deep learning orientation angle detection model alongside the CBIR feature extraction model, achieving real-time retrieval from large datasets.
Introduction of Convolutional Neural Networks has improved results on almost every image-based problem and Content-Based Image Retrieval is not an exception. But the CNN features, being rotation invariant, creates problems to build a rotation-invariant CBIR system. Though rotation-invariant features can be hand-engineered, the retrieval accuracy is very low because by hand engineering only low-level features can be created, unlike deep learning models that create high-level features along with low-level features. This paper shows a novel method to build a rotational invariant CBIR system by introducing a deep learning orientation angle detection model along with the CBIR feature extraction model. This paper also highlights that this rotation invariant deep CBIR can retrieve images from a large dataset in real-time.