Content Based Image Retrieval using Feature Extraction in JPEG Domain and Genetic Algorithm
Content-Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extends from surveillance to remote sensing, medical imaging to weather forecasting, security systems to h...
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| Main Authors: | , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2017-07-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| LEADER | 02448 am a22002893u 4500 | ||
|---|---|---|---|
| 001 | ijeecs6664_6989 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a N, Parvin |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a P, Kavitha |e author |
| 245 | 0 | 0 | |a Content Based Image Retrieval using Feature Extraction in JPEG Domain and Genetic Algorithm |
| 260 | |b Institute of Advanced Engineering and Science, |c 2017-07-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6664 | ||
| 520 | |a Content-Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extends from surveillance to remote sensing, medical imaging to weather forecasting, security systems to historical research and so on. Though extensive research is made on content based image retrieval in the spatial domain, we have most images in the internet which is JPEG compressed which pushes the need for image retrieval in the compressed domain itself rather than decoding it to raw format before comparison and retrieval. This research addresses the need to retrieve the images from the database based on the features extracted from the compressed domain along with the application of genetic algorithm in improving the retrieval results. The research focuses on various features and their levels of impact on improving the precision and recall parameters of the CBIR system. Our experimentation results also indicate that the CBIR features in compressed domain along with the genetic algorithm usage improves the results considerably when compared with the literature techniques. | ||
| 540 | |a Copyright (c) 2017 Institute of Advanced Engineering and Science | ||
| 546 | |a eng | ||
| 690 | |a Computer Science and Image retrieval | ||
| 690 | |a CT (discrete cosine transform); GA (denetic algorithm); CH (color histogram); Color moments; Precision and recall. | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 7, No 1: July 2017; 226-233 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v7.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6664/6989 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6664/6989 |z Get fulltext |