Spatial Weighting for Bag-of-Features
- 17 June 2006
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 2118-2125
- https://doi.org/10.1109/cvpr.2006.288
Abstract
International audienceThis paper presents an extension to category classification with bag-of-features, which represents an image as an orderless distribution of features. We propose a method to exploit spatial relations between features by utilizing object boundaries provided during supervised training. We boost the weights of features that agree on the position and shape of the object and suppress the weights of background features, hence the name of our method - "spatial weighting". The proposed representation is thus richer and more robust to background clutter. Experimental results show that our approach improves the results of one of the best current image classification techniques. Furthermore, we propose to apply the spatial model to object localization. Initial results are promisingKeywords
This publication has 15 references indexed in Scilit:
- A maximum entropy framework for part-based texture and object recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Discovering objects and their location in imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- Scale & Affine Invariant Interest Point DetectorsInternational Journal of Computer Vision, 2004
- Learning to detect objects in images via a sparse, part-based representationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- On the Significance of Real-World Conditions for Material ClassificationPublished by Springer Nature ,2004
- Selection of scale-invariant parts for object class recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- The Earth Mover's Distance as a Metric for Image RetrievalInternational Journal of Computer Vision, 2000
- Support vector machines for histogram-based image classificationIEEE Transactions on Neural Networks, 1999
- Feature Detection with Automatic Scale SelectionInternational Journal of Computer Vision, 1998