Summary of Texture Image Retrieval Using a Classification and Contourlet-based Features, by Asal Rouhafzay et al.
Texture image retrieval using a classification and contourlet-based features
by Asal Rouhafzay, Nadia Baaziz, Mohand Said Allili
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework enhances Content Based Image Retrieval (CBIR) for texture images by introducing a novel image representation based on the RCT-Plus transform. This transform extracts richer directional information from the image, enabling more accurate texture classification and retrieval. A learning-based approach is used to improve search efficiency, where query images are classified using an adapted similarity metric to the statistical modeling of the RCT-Plus transform. The proposed framework achieves significant improvements in retrieval rates compared to previous CBIR schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to find texture images that match a query image based on their content. This is done by creating a special representation of the image using the RCT-Plus transform, which captures more details about the direction and texture of the image. Then, a learning-based approach is used to search for the best matching images from a database. By doing so, the framework can retrieve texture images with higher accuracy compared to previous methods. |
Keywords
* Artificial intelligence * Classification
