Dynamic feature selection for joint probabilistic recognition

A method of jointly classifying a plurality of objects in data using a feature type selected from a plurality of feature types, the method comprising:

• determining classification information for each of the plurality of objects in the data by applying a predetermined joint classifier to at least one feature of a first type, the at least one feature being generated from the data using a first feature extractor, the classification information being based on a probability of each of a plurality of possible classifications;
• estimating, using the determined classification information for each of the plurality of objects in the data, an improvement in an accuracy of classification for each of the plurality of objects corresponding to a feature of a further type, the estimated improvement being a sum of a direct accuracy improvement and an indirect accuracy improvement associated with the feature of the further type, wherein the indirect accuracy improvement estimates an indirect effect of the feature of the further type on improving accuracy of classification based on a dependency between one of the objects and remaining objects of the plurality of objects, a type of each of the plurality of objects, and a number of objects in the image conditioned on the feature of the further type;
• selecting the feature of the further type, from the plurality of feature types, according to the estimated improvement in the accuracy of the classification of each of the plurality of objects;
• and
• classifying the plurality of objects in the data using the selected feature of the further type.

A non-transitory computer readable storage medium having a program recorded thereon, the program being executable by a processor to perform a method to jointly classify a plurality of objects in an image using a feature type selected from a plurality of feature types, the method comprising:

• determining classification information for each of the plurality of objects in the image by applying a predetermined joint classifier to at least one feature of a first type, the at least one feature being generated from the image using a first feature extractor, the classification information being based on a probability of each of a plurality of possible classifications;
• estimating, using determined classification information for each of the plurality of objects in the image, an improvement in an accuracy of classification for each of the plurality of objects corresponding to a feature of a further type, the estimated improvement being a sum of a direct accuracy improvement and an indirect accuracy improvement associated with the feature of the further type, wherein the indirect accuracy improvement estimates an indirect effect of the feature of the further type on improving accuracy of classification based on a dependency between one of the objects and remaining objects of the plurality of objects, a type of each of the plurality of objects, and a number of objects in the image conditioned on the feature of the further type;
• selecting the feature of the further type, from the plurality of feature types, according to the estimated improvement in the accuracy of the classification of each of the plurality of objects;
• and
• classifying the plurality of objects in the image using the selected feature of the further type.

A computer apparatus comprising a processor, a memory and a program recorded to the memory, the program being executable by the processor to perform a method to jointly classify a plurality of objects in data using a feature type selected from a plurality of feature types, the method comprising:

• determining classification information for each of the plurality of objects in the data by applying a predetermined joint classifier to at least one feature of a first type, the at least one feature being generated from the data using a first feature extractor, the classification information being based on a probability of each of a plurality of possible classifications;
• estimating, using the determined classification information for each of the plurality of objects in the data, an improvement in an accuracy of classification for each of the plurality of objects corresponding to a feature of a further type, the estimated improvement being a sum of a direct accuracy improvement and an indirect accuracy improvement associated with the feature of the further type, wherein the indirect accuracy improvement estimates an indirect effect of the feature of the further type on improving accuracy of classification based on a dependency between one of the objects and remaining objects of the plurality of objects, a type of each of the plurality of objects, and a number of objects in the image conditioned on the feature of the further type;
• selecting the feature of the further type, from the plurality of feature types, according to the estimated improvement in the accuracy of the classification of each of the plurality of objects;
• and
• classifying the plurality of objects in the data using the selected feature of the further type.