Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
International Journal of Computer Vision
From Gestalt Theory to Image Analysis: A Probabilistic Approach
From Gestalt Theory to Image Analysis: A Probabilistic Approach
An efficient color representation for image retrieval
IEEE Transactions on Image Processing
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This paper presents a novel approach for visual object classification. Based on Gestalt theory, we propose to extract features from coarse regions carrying visually significant information such as line segments and/or color and to include neighborhood information in them. We also introduce a new classification method based on the polynomial modeling of feature distribution which avoids some drawbacks of a popular approach, namely "bag of keypoints". Moreover we show that by separating features extracted from different sources in different "channels", which are then combined using a late fusion strategy, we can limit the impact of feature dimensionality and actually improve classification accuracy. Using this classifier, experiments reveal that our features lead to better results than the popular SIFT descriptors, but also that they can be combined with SIFT features to reinforce performance, suggesting that our features managed to extract information which is complementary to the one of SIFT features.