caa a22 4500 605447683 CHVBK 20210128100133.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11042-013-1800-6 doi (NATIONALLICENCE)springer-10.1007/s11042-013-1800-6 Information fusion between short term learning and long term learning in content based image retrieval systems [Elektronische Daten] [Esmat Rashedi, Hossein Nezamabadi-pour, Saeid Saryazdi] Content based image retrieval (CBIR) systems could provide more precise results by taking the user's feedbacks into account. Two types of the relevance feedback learning paradigms are short term learning (STL) and long term learning (LTL). By using both STL and LTL, a collaborative CBIR system is proposed in this paper. The proposed system introduced three fusion methods: including fusion in retrieved images, fusion in ranks, and fusion in similarities to make cooperation between STL and LTL. The proposed fusion methods are examined in a CBIR system equipped with a proposed statistical semantic clustering (SSC) method of LTL. The SSC method works based on the concept of semantic categories of the images by clustering techniques and constructing a relevancy matrix between images and semantic categories. The results of the SSC method with the suggested fusion methods are compared with two state-of-the-art LTL methods, namely virtual feature based method and dynamic semantic clustering. Comparative results confirm the efficiency of the proposed method. Furthermore, experimental results demonstrate that for a unique LTL method, various fusion methods lead to different results. Springer Science+Business Media New York, 2013 Content based image retrieval nationallicence Relevance feedback nationallicence Short term learning nationallicence Long term learning nationallicence Information fusion nationallicence Similarity function nationallicence Rashedi Esmat Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran aut Nezamabadi-pour Hossein Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran aut Saryazdi Saeid Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3799-3822 1380-7501 74:11<3799 2015 74 11042 https://doi.org/10.1007/s11042-013-1800-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1800-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Rashedi Esmat Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran aut NATIONALLICENCE 700 1- Nezamabadi-pour Hossein Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran aut NATIONALLICENCE 700 1- Saryazdi Saeid Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3799-3822 1380-7501 74:11<3799 2015 74 11042