Summary of Enhancing Historical Image Retrieval with Compositional Cues, by Tingyu Lin et al.
Enhancing Historical Image Retrieval with Compositional Cues
by Tingyu Lin, Robert Sablatnig
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 This paper proposes a novel approach to image retrieval by incorporating composition-related information extracted using Convolutional Neural Networks (CNNs). Existing methods focus on semantic information, but neglect non-semantic factors that are crucial for flexible exploration across various themes. The authors introduce a compositional aspect into the retrieval model, considering both composition rules and semantic information. Experimental results show that the proposed method outperforms traditional content-based approaches, enabling more accurate image identification in databases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine looking at old photos of your ancestors, but instead of just seeing pictures, you want to find specific images related to a particular event or theme. This paper helps make it possible by developing a new way to search through huge collections of digital images based on how they are composed, not just what’s in them. |
