Akademik Veri Yönetim Sistemi
Neurocomputing, cilt.671, 2026 (SCI-Expanded, Scopus)
Deep learning-based image retrieval (IR) approaches promising automatic feature extraction suffer from several limitations, including insufficient semantic representation, suboptimal retrieval performance, and limited evaluation across different hash code lengths. To address these limitations, a novel deep learning-based Autoencoder Hashing IR (AHIR) algorithm is proposed, employing the strengths of ResNet50 and autoencoder architectures. In this integrated model, ResNet50 is responsible for extracting the semantic features of images, while the autoencoder compresses these features to the required dimensions and transforms them into hash codes. The study's contributions include the ability to capture both low-level and high-level features, streamline IR for large-scale databases, and enhance efficiency in supervised learning scenarios. Furthermore, a comparative analysis of various reported IR algorithms is presented, highlighting the performance of AHIR against its counterparts for MS-COCO, NUS-WIDE, and MIRFLICKR-25K datasets. AHIR outperforms the existing methods with the highest mAP scores of 0.9103, 0.9007, and 0.9136 for MS-COCO, NUS-WIDE, and MIRFLICKR-25K, respectively. The results manifest the superior IR performance of AHIR thanks to the novel integrated autoencoder-based hashing mechanism.