High recall additive pattern recognition for image and other applications

1. A computer-implemented method, comprising:

testing a first Support Vector Machine (SVM model on a feature matrix T to produce false positive (FP) data set and false negative (FN) data set by a computer processor, wherein the first SVM model comprises SVM support vector number of support vectors, wherein the feature matrix T includes n feature vectors of length m, wherein n and m are integer numbers;

automatically removing feature vectors corresponding to the FP data set from the feature matrix T by the computer processor to produce a feature matrix T_best, wherein the feature matrix T_best includes T_best sample number of sample points;

retraining the first SVM model on the feature matrix T_best to produce a second SVM model;

checking if a ratio (T_best sample number)/(SVM support vector number) is above a threshold for the second SVM model on the feature matrix T_best; and

if the ratio is above the threshold, performing SVM predictions using the second SVM model on the feature matrix T_best.

2. The computer-implemented method of claim 1 , wherein the n feature vectors are based on faces or objects in images, the method further comprising:

classifying the faces or the objects in the images using the second SVM model on the feature matrix T_best.

3. The computer-implemented method of claim 2 , further comprising:

creating an image-product design based on the faces or the objects in the images classified by the second SVM model using the feature matrix T_best.

4. The computer-implemented method of claim 1 , further comprising:

if the ratio (T_best sample number)/(SVM support vector number) is not above the threshold for the SVM model on T_best, repeating steps of selecting, testing, automatically removing, retraining, and checking to find a third SVM model having (T_best sample number)/(SVM support vector number) ratio that exceeds the threshold.

5. The computer-implemented method of claim 4 , further comprising:

performing SVM predictions using the third SVM model on the feature matrix T_best.

6. The computer-implemented method of claim 4 , further comprising:

if a SVM model having (T_best sample number)/(SVM support vector number) ratio that exceeds the threshold is not found, selecting a fourth SVM model that yields a highest (T_best sample number)/(SVM support vector number) ratio in repeating steps of selecting, testing, automatically removing, retraining, and checking; and

performing SVM predictions using the fourth SVM model on the feature matrix T_best.

7. The computer-implemented method of claim 1 , wherein the threshold for the (T_best sample number)/(SVM support vector number) ratio is ten or higher.

8. The computer-implemented method of claim 1 , further comprising:

applying Grid Search on the feature matrix T to find optimal kernel parameters before testing the first SVM model on a feature matrix T.

9. The computer-implemented method of claim 1 , wherein the feature vectors are based on faces or objects in images, the computer-implemented method further comprising:

classifying the objects and the faces in the images by performing SVM predictions on the feature matrix T_best.

10. The computer-implemented method of claim 9 , further comprising:

automatically creating designs for photo products based on the objects and faces classified by performing SVM predictions on the feature matrix T_best.

11. The computer-implemented method of claim 1 , further comprising:

selecting a feature vector v from the FP data set to add back to the feature matrix T_best to produce a feature matrix T_v; and

retraining the second SVM model on the feature matrix T_v to produce a fifth SVM model.

12. The computer-implemented method of claim 11 , further comprising:

calculating a benefit function to produce a benefit function value, wherein the benefit function is dependent of differences between false positives, false negatives, and numbers of support vectors generated on the feature matrix T_best by the second SVM model and on T_v respectively by the fifth SVM model;

adding the feature vector v to T_best if the benefit function value meets a predetermined criterion; and

performing SVM prediction using the fifth SVM model on the feature matrix T_best.

13. The computer-implemented method of claim 11 , wherein the n feature vectors are based on faces or objects in images, the method further comprising:

classifying the faces or the objects in the images using the fifth SVM model on the feature matrix T_best.

14. The computer-implemented method of claim 13 , further comprising:

creating an image-product design based on the faces or the objects in the images classified by the fifth SVM model using the feature matrix T_best.

15. The computer-implemented method of claim 11 , further comprising:

repeating steps of selecting a feature vector v, training the second SVM model, performing SVM predictions, calculating a benefit function, and adding the feature vector v to the feature matrix T_best by selecting and adding a different feature vector from the FP data set to the feature matrix T_best,

wherein SVM predictions are performed using the feature matrix T_best that gives a highest best function value.

16. The computer-implemented method of claim 15 , wherein all the feature vectors corresponding to the FP data set are evaluated by calculating a corresponding benefic function and determined to be added to the feature matrix T_best or not depending on a value of the corresponding benefic function.

17. The computer-implemented method of claim 16 , wherein the feature vector v is not added to the feature matrix T_best if the value of the benefit function does not meet the predetermined criterion.

18. The computer-implemented method of claim 16 , wherein the feature vectors are based on faces or objects in images, the computer-implemented method further comprising:

classifying the objects and the faces in the images by performing SVM predictions on the feature matrix T_best.