MLMQ-IR: Multi-label multi-query image retrieval based on the variance of Hamming distance

Akbacak E., TOKTAŞ A., Erkan U., Gao S.

Knowledge-Based Systems, cilt.283, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 283
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.knosys.2023.111193
  • Dergi Adı: Knowledge-Based Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA)
  • Anahtar Kelimeler: CNN, Deep learning, Hash code, Image retrieval, Multi-label image retrieval, Multi-query image retrieval, Variance
  • Ankara Üniversitesi Adresli: Evet

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.