Xerox Corporation

Jakob Verbeek

Gabriela Csurka

Thomas Mensink

Florent C Perronnin

A classification system and method enable improvements to classification with nearest class mean classifiers by computing a comparison measure between a multidimensional representation of a new sample and a respective multidimensional class representation embedded into a space of lower dimensionality than that of the multidimensional representations. The embedding is performed with a projection that has been learned on labeled samples to optimize classification with respect to multidimensional class representations for classes which may be the same or different from those used subsequently for classification. Each multidimensional class representation is computed as a function of a set of multidimensional representations of labeled samples, each labeled with the respective class. A class is assigned to the new sample based on the computed comparison measures.

Metric learning for nearest class mean classifiers

A system and a method are provided for labeling images and for generating an annotation system. The labeling method includes providing a graphical structure, such as a tree structure, which graphically represents predictive correlations between labels in a set of labels. The predictive correlations can, for example, estimate the likelihood, in a training set, that knowing one label has a given value, another label will have a given value. An image to be labeled is received. Feature-based predictions for values of labels in the set of labels are computed for the image. A value for at least one label for the image from the set of labels is computed based on the feature-based label predictions and inference on the structured prediction model.

Learning structured prediction models for interactive image labeling

In a retrieval application, a document relevance scoring function comprises a weighted combination of scoring components including at least one of a pseudo-relevance scoring component and a cross-media relevance scoring component. Weights of the document relevance scoring function are optimized to generate a trained document relevance scoring function. The optimizing is respective to a set of training documents including at least some multimedia training documents and a set of training queries and corresponding training document relevance annotations. A retrieval operation is performed for an input query respective to a database using the trained document relevance scoring function to retrieve one or more documents from the database.

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Grenoble, France

Jakob Verbeek