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Aggregating Local Features of Convolutional Neural Network for Material Image Retrieval
| Content Provider | Scilit |
|---|---|
| Author | Qing, Qing |
| Copyright Year | 2021 |
| Description | Journal: Journal of Physics: Conference Series Large-scale microscopic images in materials science need to be indexed and managed using practical management tools. Content-based Image Retrieval (CBIR), which indexes and searches images based on the image features, allows for long-term data management in large-scale image datasets. Considering the difference between material microscopy images and natural ones, we propose a novel CBIR method for material microscopic images. In the proposed method, convolutional neural networks (CNN) are used to extract local features from an image, and the scale-invariant feature transform (SIFT) model is used to generate a keypoint density map (KDM). Experiments on a material microscopic image dataset show that the proposed method achieves an approving retrieval performance. |
| Related Links | https://iopscience.iop.org/article/10.1088/1742-6596/1948/1/012061/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1948/1/012061 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 1 |
| Volume Number | 1948 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2021-06-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Industrial Engineering Material Microscopic Images |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
