SPECTRAL ALGORITHM FOR CONTENT-BASED IMAGE RETRIEVAL
Colour images are rich in visual information. The process of searching for the most similar images in large-scale database based on visual features of query image is still a challenge in Content-Based Image Retrieval (CBIR) due to a semantic gap issue. In this paper, we proposed a fusing retrieval...
| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
Mustansiriyah University/College of Engineering
2020-09-01
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| Series: | Journal of Engineering and Sustainable Development |
| Subjects: | |
| Online Access: | https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/106 |
| Summary: | Colour images are rich in visual information. The process of searching for the most similar images in large-scale database based on visual features of query image is still a challenge in Content-Based Image Retrieval (CBIR) due to a semantic gap issue. In this paper, we proposed a fusing retrieval method to diminish the gap between high-level and low-level meanings by involving two aspects. The first aspect is increasing the effectiveness of image representation. Hence, data-level fusion features were suggested, a local feature from Discrete Cosine Transform (DCT) and Local Binary Patterns (LBP) in frequency and spatial domains respectively that was applied by a spectral clustering algorithm (graph-based) in addition to a global weighted LBP feature. The second aspect is fusing multiple retrieved similarity measures (scores/evidences) obtained from above global (LBP) and local features (DCTLBP) in terms of score-level fusion. The method is evaluated in WANG standard publically dataset.
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| ISSN: | 2520-0917 2520-0925 |