Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
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
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Marginal-based visual alphabets for local image descriptors aggregation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
VQ Codebook Design Algorithm Based on Copula Estimation of Distribution Algorithm
RVSP '11 Proceedings of the 2011 First International Conference on Robot, Vision and Signal Processing
Exploring two spaces with one feature: kernelized multidimensional modeling of visual alphabets
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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Local Image Descriptors (LID) aggregation models such as Bag of Words and Fisher Vectors represent an image based on the distribution of its LIDs given a global model, e.g. a visual codebook or a Gaussian Mixture. Inspired by Copula theory, in this paper we propose a LID-based feature that represents directly the behavior of the image LID distribution, without requiring to compute a global model. Following the definition of Copula, we represent the distribution of the image LIDs by describing, on one side, its marginals, and on the other side, a Copula function. The Copula defines the dependencies between the marginals and their mapping to a multivariate probability distribution function. We test the resulting feature for scene recognition and video retrieval (Trecvid data), showing that our approach outperforms, in both tasks, the Bag of Words and the Fisher Vectors Model.