Hybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion

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dc.contributor.authorKürşat, Murat
dc.contributor.authorKaraduman, Mücahit
dc.contributor.authorMutlu, Hursit Burak
dc.contributor.authorEmre, İrfan
dc.contributor.authorYıldırım, Muhammed
dc.date.accessioned2025-10-24T17:59:09Z
dc.date.available2025-10-24T17:59:09Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractContent-based Image Retrieval (CBIR) systems have been used frequently in recent years, along with developing technology. Especially in large datasets, retrieval-based systems produce more successful results. This study created a dataset consisting of 27 different Euphorbia seed types belonging to the same genus. It is difficult for Convolutional Neural Network (CNN) architectures to produce successful results in the created dataset. In addition, the high computational and memory requirements of CNN architectures have further increased the need for CBIR systems in large datasets. Therefore, a hybrid retrieval system was developed to make inferences from 27 different seed images. In the developed system, feature extraction was performed using Darknet53, Xception, and Densenet201 architectures. These extracted features were concatenated to bring together different features of the same image. Then, unnecessary features were eliminated from the combined features with the Neighborhood Component Analysis (NCA) method. The cosine similarity measurement metric was used to measure the similarity between the query image and other images. Precision-recall curves and Average Precision (AP) metrics were used to measure the performance of the proposed retrieval-based system. In the study, an average AP value of 0.96809 was obtained. The morphology of the seeds is a critical characteristic of Euphorbia, and this work has validated the artificial intelligence methodology.
dc.identifier.doi10.15832/ankutbd.1661676
dc.identifier.endpage940
dc.identifier.issn1300-7580
dc.identifier.issn2148-9297
dc.identifier.issue4
dc.identifier.startpage931
dc.identifier.urihttps://doi.org/10.15832/ankutbd.1661676
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1918
dc.identifier.volume31
dc.language.isoen
dc.publisherAnkara Üniversitesi
dc.relation.ispartofTarım Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzDergiPark_20251023
dc.subjectKnowledge Representation and Reasoning
dc.subjectBilgi Temsili ve Akıl Yürütme [EN] Autonomous Agents and Multiagent Systems
dc.subjectOtonom Ajanlar ve Çok Yönlü Sistemler
dc.titleHybrid Content-Based Image Retrieval System for a Comprised 27-Class Euphorbia Seed Dataset Using Deep Feature Fusion
dc.typeArticle

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