Image Similarity using An Ensemble of Context-Sensitive Models
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916387334651904 |
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| author | Liao, Zukang Chen, Min |
| author_facet | Liao, Zukang Chen, Min |
| contents | Image similarity has been extensively studied in computer vision. In recent years, machine-learned models have shown their ability to encode more semantics than traditional multivariate metrics. However, in labelling semantic similarity, assigning a numerical score to a pair of images is impractical, making the improvement and comparisons on the task difficult. In this work, we present a more intuitive approach to build and compare image similarity models based on labelled data in the form of A:R vs B:R, i.e., determining if an image A is closer to a reference image R than another image B. We address the challenges of sparse sampling in the image space (R, A, B) and biases in the models trained with context-based data by using an ensemble model. Our testing results show that the ensemble model constructed performs ~5% better than the best individual context-sensitive models. They also performed better than the models that were directly fine-tuned using mixed imagery data as well as existing deep embeddings, e.g., CLIP and DINO. This work demonstrates that context-based labelling and model training can be effective when an appropriate ensemble approach is used to alleviate the limitation due to sparse sampling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07951 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Image Similarity using An Ensemble of Context-Sensitive Models Liao, Zukang Chen, Min Computer Vision and Pattern Recognition Image similarity has been extensively studied in computer vision. In recent years, machine-learned models have shown their ability to encode more semantics than traditional multivariate metrics. However, in labelling semantic similarity, assigning a numerical score to a pair of images is impractical, making the improvement and comparisons on the task difficult. In this work, we present a more intuitive approach to build and compare image similarity models based on labelled data in the form of A:R vs B:R, i.e., determining if an image A is closer to a reference image R than another image B. We address the challenges of sparse sampling in the image space (R, A, B) and biases in the models trained with context-based data by using an ensemble model. Our testing results show that the ensemble model constructed performs ~5% better than the best individual context-sensitive models. They also performed better than the models that were directly fine-tuned using mixed imagery data as well as existing deep embeddings, e.g., CLIP and DINO. This work demonstrates that context-based labelling and model training can be effective when an appropriate ensemble approach is used to alleviate the limitation due to sparse sampling. |
| title | Image Similarity using An Ensemble of Context-Sensitive Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.07951 |