INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Nowadays digital imaging has become an essential segment in many applications such as medical imaging, remote sensing, crime prevention, education, multimedia, data mining etc. These applications require digital images as a source for various processes like segmentation, object recognition, tracking and others. To index and search suitable images from the rapidly increasing digital image collections, an image retrieval system is used. An image is retrieved in CBIRS system by adopt numerous techniques all together such as integrating Pixel Cluster Indexing and Histogram Intersection. In this thesis work, we propose to design and implement a technique of CBIRS to be less complex and highly accurate retrieval system. Here score level fusion is used with machine learning to improve precision in CBIRS. The proposed framework initially selects pertinent images from a large databases using color moment information. Color information can be extracted from the image by both global and local techniques. conseq
"Efficient CBIRS Using Fusion and Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 8, page no.397 - 403, August-2020, Available :http://www.ijsdr.org/papers/IJSDR2008055.pdf
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Publication Details:
Published Paper ID: IJSDR2008055
Registration ID:192374
Published In: Volume 5 Issue 8, August-2020
DOI (Digital Object Identifier):
Page No: 397 - 403
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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