Sentence examples for features representation from inspiring English sources

Facial shape representation can be categorized into three classes, namely: local features representation, global feature representation and hybrid representations.

This paper focuses on recognizing the human interaction relative to human emotion, and addresses the problem of interaction features representation.

The authors also discuss some existing text representation approaches in terms of features, representation model, and application task.

Features representation, selection and weighting are reported and classification effectiveness is evaluated on the four classes using confusion matrices.

Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation.

The features representation can be modified and annotated later by sending an appropriate RDF representation to the feature URI via a HTTP PUT command.

Furthermore, we introduce an effective feature representation for infrared objects.

We then chose the first ten frequencies for feature representation.

These terms allow for a more discriminatory shared feature representation.

LDABoost.1 uses VSM model and words for feature representation.

The remarkable difference is the feature representation for a face.