Summary of Image Retrieval Methods in the Dissimilarity Space, by Madhu Kiran et al.
Image Retrieval Methods in the Dissimilarity Space
by Madhu Kiran, Kartikey Vishnu, Rafael M. O. Cruz, Eric Granger
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 research paper presents a novel approach to image retrieval, focusing on improving backbone feature extraction models trained using metric learning. The authors aim to enhance the accuracy of person re-identification in real-world video analytics and surveillance applications, where existing deep learning (DL) models struggle due to limitations such as the curse of dimensionality, overfitting, and sensitivity to noisy data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image retrieval is a challenging task that relies on training backbone feature extraction models using metric learning. The goal is to extract discriminant queries and reference (gallery) feature representations for similarity matching. While deep learning (DL) models have achieved state-of-the-art accuracy in many applications, they still struggle in real-world scenarios. |
Keywords
» Artificial intelligence » Deep learning » Feature extraction » Overfitting
