MLMQ-IR: Multi-label multi-query image retrieval based on the variance of Hamming distance
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier B.V.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Image retrieval (IR) methods extract the most relevant images to the query images from an image database. The existing IR methods, which retrieve images with a low degree of similarity, use computationally expensive approaches. In this study, a novel Multi-Label Multi-Query IR (MLMQ-IR) method based on the variance of Hamming distance is presented for the query of multiple images having multiple labels. The MLMQ-IR uses deep learning-based hashing code generation with ResNet50 structure. The variance evaluates the minimum variation of the distances between the query and database images, providing the trade-off images according to the center of Pareto space. Moreover, the MLMQ-IR exploits a new Triple Loss Multi-Label Hashing (TLMH) depending on binary cross-entropy loss and bit-balance loss functions. The MLMQ-IR is compared with recent multi-label and multi-query methods through MIRFLICKR-25 K, MS-COCO and NUS-WIDE datasets in terms of three well-known metrics, and the methods are ranked with succussed sorting. As a result, the MLMQ-IR method has the best avg. mean rank with 1.86. The results manifest that the MLMQ-IR provides the most similar retrieved images to the query images owing to utilizing the variance which is the efficient and fast IR approach. © 2023 Elsevier B.V.
Açıklama
Anahtar Kelimeler
CNN, Deep learning, Hash code, Image retrieval, Multi-label image retrieval, Multi-query image retrieval, Variance, Economic and social effects, Hamming distance, Hash functions, Image retrieval, Query processing, Deep learning, Hash code, Image database, Label images, Multi-label image retrieval, Multi-labels, Multi-query image retrieval, Query images, Retrieval methods, Variance, Deep learning
Kaynak
Knowledge-Based Systems
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
283
