Few but Informative Local Hash Code Matching for Image Retrieval

TY - GEN

T1 - Few but Informative Local Hash Code Matching for Image Retrieval

AU - Hu, Zechao

AU - Bors, Adrian Gheorghe

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

PY - 2023/6/4

Y1 - 2023/6/4

N2 - Content-based image retrieval (CBIR) aims to search for the most similar images from an extensive database to a given query content. Existing CBIR works either represent each image with a compact global feature vector or extract a large number of highly compressed low-dimensional local features, where each contains limited information. In this research study, we propose an expressive local feature extraction pipeline and a many-to-many local feature matching method for large-scale CBIR. Unlike existing local feature methods, which tend to extract large amounts of low-dimensional local features from each image, the proposed method models characteristic feature representations for each image, aiming to employ fewer but more expressive local features. For further improving the results, an end-to-end trainable hash encoding layer is used for extracting compact but informative codes from images. The proposed many-to-many local feature matching is then directly performed on the hash feature vectors from input images, leading to new state-of-the-art performance on several benchmark datasets.

AB - Content-based image retrieval (CBIR) aims to search for the most similar images from an extensive database to a given query content. Existing CBIR works either represent each image with a compact global feature vector or extract a large number of highly compressed low-dimensional local features, where each contains limited information. In this research study, we propose an expressive local feature extraction pipeline and a many-to-many local feature matching method for large-scale CBIR. Unlike existing local feature methods, which tend to extract large amounts of low-dimensional local features from each image, the proposed method models characteristic feature representations for each image, aiming to employ fewer but more expressive local features. For further improving the results, an end-to-end trainable hash encoding layer is used for extracting compact but informative codes from images. The proposed many-to-many local feature matching is then directly performed on the hash feature vectors from input images, leading to new state-of-the-art performance on several benchmark datasets.

U2 - 10.1109/ICASSP49357.2023.10096802

DO - 10.1109/ICASSP49357.2023.10096802

M3 - Conference contribution

BT - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

ER -