Abstract:
The objective of this paper is the unsupervised segmentation of image training sets into foreground and background in order to improve image classification performance. T...Show MoreMetadata
Abstract:
The objective of this paper is the unsupervised segmentation of image training sets into foreground and background in order to improve image classification performance. To this end we introduce a new scalable, alternation-based algorithm for co-segmentation, BiCoS, which is simpler than many of its predecessors, and yet has superior performance on standard benchmark image datasets.
Published in: 2011 International Conference on Computer Vision
Date of Conference: 06-13 November 2011
Date Added to IEEE Xplore: 12 January 2012
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Image Classification ,
- Cosegmentation Methods ,
- Training Set ,
- Support Vector Machine ,
- Color Distribution ,
- Reasons For Success ,
- Background In Order ,
- Training Data ,
- Classification Accuracy ,
- Image Segmentation ,
- Random Fields ,
- Mixture Model ,
- Number Of Images ,
- Training Images ,
- Multiple Images ,
- Recognition Accuracy ,
- Segmentation Accuracy ,
- Geometric Model ,
- Learning Spaces ,
- Linear Support Vector Machine ,
- Standard Support Vector Machine ,
- Discriminative Learning ,
- Conditional Random Field ,
- Ground Truth Segmentation ,
- Flower Species ,
- Higher Segmentation Accuracy ,
- Superpixel Segmentation ,
- Image Recognition ,
- Geometric Shapes
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Image Classification ,
- Cosegmentation Methods ,
- Training Set ,
- Support Vector Machine ,
- Color Distribution ,
- Reasons For Success ,
- Background In Order ,
- Training Data ,
- Classification Accuracy ,
- Image Segmentation ,
- Random Fields ,
- Mixture Model ,
- Number Of Images ,
- Training Images ,
- Multiple Images ,
- Recognition Accuracy ,
- Segmentation Accuracy ,
- Geometric Model ,
- Learning Spaces ,
- Linear Support Vector Machine ,
- Standard Support Vector Machine ,
- Discriminative Learning ,
- Conditional Random Field ,
- Ground Truth Segmentation ,
- Flower Species ,
- Higher Segmentation Accuracy ,
- Superpixel Segmentation ,
- Image Recognition ,
- Geometric Shapes
