Publications
2017
1.
Fidalgo, Eduardo
Selection of relevant information to improve Image Classification using Bag of Visual Words Artículo de revista
En: ELCVIA: electronic letters on computer vision and image analysis, vol. 16, no 2, pp. 5–8, 2017.
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, feature selection, Fine-grained Recognition, Image classification
@article{fidalgo_selection_2017,
title = {Selection of relevant information to improve Image Classification using Bag of Visual Words},
author = {Eduardo Fidalgo},
url = {https://recercat.cat/handle/2072/428567},
year = {2017},
date = {2017-01-01},
journal = {ELCVIA: electronic letters on computer vision and image analysis},
volume = {16},
number = {2},
pages = {5–8},
abstract = {This PhD thesis addresses one of the main challenges in computer vision: image classification. With the rapid growth in the number of images, reliable classification has become increasingly important. The conventional classification pipeline involves extracting local image features, encoding them into a feature vector, and classifying them using a pre-trained model. The Bag of Words model and its extensions, such as pyramid matching and weighted schemes, have shown strong results. However, errors can occur in any step of the process, which may lead to a decline in classification performance. These errors can stem from multiple objects in an image, thin or small objects, incorrect annotations, or fine-grained recognition tasks. The thesis demonstrates that selecting high-quality features can significantly improve fine-grained classification, showing that a large training dataset is not always necessary to achieve good results.},
keywords = {Computer vision, feature selection, Fine-grained Recognition, Image classification},
pubstate = {published},
tppubtype = {article}
}
This PhD thesis addresses one of the main challenges in computer vision: image classification. With the rapid growth in the number of images, reliable classification has become increasingly important. The conventional classification pipeline involves extracting local image features, encoding them into a feature vector, and classifying them using a pre-trained model. The Bag of Words model and its extensions, such as pyramid matching and weighted schemes, have shown strong results. However, errors can occur in any step of the process, which may lead to a decline in classification performance. These errors can stem from multiple objects in an image, thin or small objects, incorrect annotations, or fine-grained recognition tasks. The thesis demonstrates that selecting high-quality features can significantly improve fine-grained classification, showing that a large training dataset is not always necessary to achieve good results.
2.
Fidalgo, Eduardo
Selection of relevant information to improve Image Classification using Bag of Visual Words Artículo de revista
En: 2017.
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, feature selection, Fine-grained Recognition, Image classification
@article{fidalgo_selection_2017-2,
title = {Selection of relevant information to improve Image Classification using Bag of Visual Words},
author = {Eduardo Fidalgo},
url = {https://www.raco.cat/index.php/ELCVIA/article/view/v16-n2-fidalgo},
year = {2017},
date = {2017-01-01},
abstract = {This PhD thesis tackles a major challenge in computer vision: image classification. With the rapid increase in the number of images, it is essential to classify them accurately and efficiently. The typical classification pipeline involves extracting image features, encoding them into vectors, and classifying them with a pre-trained model. The Bag of Words model and its variants, such as pyramid matching and weighted schemes, have proven to be effective. However, errors can occur at any stage, causing performance issues, especially when dealing with multiple objects, small or thin items, incorrect annotations, or fine-grained recognition. The thesis highlights the importance of good feature selection to enhance classification performance, showing that high-quality features can lead to improved fine-grained classification without requiring extensive training datasets.},
keywords = {Computer vision, feature selection, Fine-grained Recognition, Image classification},
pubstate = {published},
tppubtype = {article}
}
This PhD thesis tackles a major challenge in computer vision: image classification. With the rapid increase in the number of images, it is essential to classify them accurately and efficiently. The typical classification pipeline involves extracting image features, encoding them into vectors, and classifying them with a pre-trained model. The Bag of Words model and its variants, such as pyramid matching and weighted schemes, have proven to be effective. However, errors can occur at any stage, causing performance issues, especially when dealing with multiple objects, small or thin items, incorrect annotations, or fine-grained recognition. The thesis highlights the importance of good feature selection to enhance classification performance, showing that high-quality features can lead to improved fine-grained classification without requiring extensive training datasets.