GENERATION OF VISUAL PATTERN CLASSES FOR VISUAL PATTERN REGONITION
Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.
1 . A system comprising:
at least one processor; and
memory comprising instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
generating, for each of a plurality of reference visual patterns, at least one representation of the reference visual pattern based on a plurality of local feature types;
generating at least one image classifier based on the at least one representation of each of the reference visual patterns;
classifying each of the reference visual patterns into at least one of a plurality of visual pattern classifications using the at least one image classifier; and
assigning a reference visual pattern of the plurality of reference visual patterns into at least two visual pattern classifications of the plurality of visual pattern classifications, where the assigned reference visual pattern is classified into any of the at least two visual pattern classifications of the plurality of visual pattern classifications.
2 . The system of claim 1 , wherein:
the plurality of visual pattern classifications are organized hierarchically.
3 . The system of claim 1 , wherein:
the plurality of visual pattern classifications are organized nonhierarchically.
4 . The system of claim 1 , wherein:
the plurality of local feature types comprises at least one of a group consisting of scale-invariant feature transform (SIFT) descriptors, local binary pattern (LBP) descriptors, and kernel descriptors.
5 . The system of claim 1 , wherein the generating of the at least one representation of the reference visual pattern comprises:
generating, for each of a plurality of pixel blocks of the reference visual pattern, a local feature representation for each of the plurality of local feature types; and
combining, for each of the plurality of pixel blocks of the reference visual pattern, the local feature representations for the plurality of local feature types to produce a second local feature representation for each of the plurality of pixel blocks of the reference visual pattern, wherein the generating of the at least one image classifier is based on the second local feature representation for each of the plurality of pixel blocks of the reference visual pattern.
6 . The system of claim 5 , wherein:
the combining of the local feature representations for the plurality of local feature types comprises concatenating the local feature representations for the plurality of local feature types to produce the second local feature representation.
7 . The system of claim 1 , wherein:
the generating of the at least one representation of the reference visual pattern comprises generating a representation of the reference visual pattern for each of the plurality of local feature types;
the generating of the at least one image classifier comprises generating a joint weight vector corresponding to the plurality of local feature types based on the feature representation for each of the plurality of local feature types; and
the generating of the at least one image classifier is based on the joint weight vector.
8 . The system of claim 1 , wherein:
the generating of the at least one representation of the reference visual pattern comprises generating a feature representation of the reference visual pattern for each of the plurality of local feature types; and
the generating of the at least one image classifier comprises:
generating a separate weight vector for each of the plurality of local feature types based on the feature representation for each of the plurality of local feature types; and
combining the separate weight vectors to produce a joint weight vector, wherein the generating of the at least one image classifier is based on the joint weight vector.
9 . The system of claim 1 , the operations further comprising:
classifying a plurality of candidate visual patterns based on the at least one image classifier.
10 . The system of claim 9 , wherein two or more image classifiers classify the plurality of candidate visual patterns into classes defined by a first set of parent nodes and at least a second set of child nodes.
11 . The system of claim 10 , wherein the child nodes include at least one auxiliary node for previously misclassified images or images properly concurrently classified in two or more nodes.
12 . The system of claim 11 , wherein the auxiliary node is for misclassified images, and the auxiliary node includes images drawn from mutually exclusive sibling nodes.
13 . The system of claim 1 , wherein the one or more image classifiers classify the reference visual patterns into visual pattern classifications comprising a first set of parent nodes and at least a second set of child nodes.
14 . The system of claim 13 , wherein the child nodes include at least one auxiliary node for previously misclassified images or images properly concurrently classified in two or more nodes.
15 . The system of claim 14 , wherein the auxiliary node is for misclassified images, and the auxiliary node includes images drawn from mutually exclusive sibling nodes.