International Journal of Computer Vision
An information-theoretic analysis of hard and soft assignment methods for clustering
Learning in graphical models
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical evaluation of dissimilarity measures for color and texture
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Introduction to Information Retrieval
Introduction to Information Retrieval
Signature quadratic form distances for content-based similarity
MM '09 Proceedings of the 17th ACM international conference on Multimedia
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
The state of the art in image and video retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Signature Quadratic Form Distance
Proceedings of the ACM International Conference on Image and Video Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling image similarity by Gaussian mixture models and the Signature Quadratic Form Distance
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We introduce a new family of flexible feature representations for content-based multimedia retrieval: probabilistic feature signatures. While conventional feature histograms and feature signatures aggregate the multimedia objects' feature distributions exhibited in some feature space according to a partitioning, probabilistic feature signatures model these feature distributions by means of discrete or continuous probability distributions. In this way, they combine the advantages of high expressiveness and compactness, for instance through Gaussian mixture models. In this paper, we introduce the concept of probabilistic feature signatures and provide the empirical evidence of high retrieval performance when using this feature representation type. We show that probabilistic feature signatures are able to outperform conventional feature signatures.