Similar Image Retrieval Technique based on Semantics through Automatic Labeling Extraction of Personalized Images

• Published in: Journal of Information and Communication Convergence Engineering, 22(1) Vol. 22; no. 1; pp. 56 - 63
• Main Author: Jung-Hee Seo
• Format: Journal Article
• Language: English
• Published: 한국정보통신학회JICCE 2024; 한국정보통신학회
• Subjects: 전자/정보통신공학; CNN; Image Retrieval; Feature Detect; etc; Deep Learning; CBIR
• ISSN: 2234-8255; 2234-8883
• DOI: 10.56977/jicce.2024.22.1.56

Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-basedimage retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device. KCI Citation Count: 0