Detail publikace

Automatic Image Labelling using Similarity Measures

Originální název

Automatic Image Labelling using Similarity Measures

Anglický název

Automatic Image Labelling using Similarity Measures

Jazyk

en

Originální abstrakt

Scene classification based on global features. It can be used, for example, for annotating large databases of photos. The whole process has several steps. The first step is features extraction, and then the distance between a new image and reference images is calculated. A model is trained to classify new images based on this distance. The model was created using the Naïve Bayes classifier. To improve accuracy the forward selection was used, which optimizes the selection of a group of attributes. The overall performance on the testing dataset was 69.76%.

Anglický abstrakt

Scene classification based on global features. It can be used, for example, for annotating large databases of photos. The whole process has several steps. The first step is features extraction, and then the distance between a new image and reference images is calculated. A model is trained to classify new images based on this distance. The model was created using the Naïve Bayes classifier. To improve accuracy the forward selection was used, which optimizes the selection of a group of attributes. The overall performance on the testing dataset was 69.76%.

BibTex


@inproceedings{BUT109408,
  author="Václav {Uher} and Radim {Burget} and Jan {Karásek} and Jan {Mašek} and Malay Kishore {Dutta}",
  title="Automatic Image Labelling using Similarity Measures",
  annote="Scene classification based on global features. It can be used, for example, for annotating large databases of photos. The whole process has several steps. The first step is features extraction, and then the distance between a new image and reference images is calculated. A model is trained to classify new images based on this distance. The model was created using the Naïve Bayes classifier. To improve accuracy the forward selection was used, which optimizes the selection of a group of attributes. The overall performance on the testing dataset was 69.76%.",
  address="IEEE",
  booktitle="MEDCOM 2014 CD-ROM",
  chapter="109408",
  doi="10.1109/MedCom.2014.7005984",
  howpublished="electronic, physical medium",
  institution="IEEE",
  year="2015",
  month="january",
  pages="101--104",
  publisher="IEEE",
  type="conference paper"
}