A computer implemented method of automatically creating a classification function trained with augmented representation of features extracted from a plurality of sample media objects using one or more hardware processors for executing a code. The code comprises code instructions for extracting a plurality of features from a plurality of sample media objects, generating a plurality of feature samples for each of the plurality of features by augmenting the plurality of features, training a classification function with the plurality of features samples and outputting the classification function for classifying one or more new media objects.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method of automatically creating a classification function trained with augmented representation of features extracted from a plurality of sample media objects, comprising; using at least one hardware processor for executing a code comprising code instructions for performing the following: extracting a plurality of features from a plurality of sample media objects; generating a plurality of feature samples for each one of said plurality of features by augmenting said plurality of features; training a classification function with said plurality of features samples; and outputting said classification function for classifying at least one new media object. 2. The computer implemented method of claim 1, wherein said classification function may perform as at least one of: a regression function and a clustering function. 3. The computer implemented method of claim 1, wherein said classification function is a convolutional neural network (CNN). 4. The computer implemented method of claim 1, wherein said plurality of features is extracted by submitting said plurality of sample media objects to a CNN during a learning process of said CNN. 5. The computer implemented method of claim 1, further comprising a topology of said classification function is optimized during said extraction of said plurality of features according to a size of each of said plurality of sample media objects. 6. The computer implemented method of claim 1, wherein said augmentation includes applying at least one transformation to each of said plurality of features. 7. The computer implemented method of claim 6, further comprising said at least one transformation is a member selected from a group consisting of: a rotation, a shift, a translation, a scaling, a reflection, a geometric transformation, a pixel flip, a noise injection, a speed perturbation, a pitch shifting, a time stretching, a gain induction and a frequency spectrum warpage. 8. A system for creating a classification function trained with augmented representations of features extracted from a plurality of sample media objects, comprising: an interface module; a program store storing a code; and at least one processor coupled to said interface and said program store for executing said stored code, said code comprising: code instructions to extract a plurality of features from a plurality of sample media objects; code instructions to generate a plurality of features samples for each one of said plurality of features by augmenting said plurality of features; code instructions to train a classification function with said plurality of features samples; and code instructions to output said classification function for classifying at least one new media object. 9. The system of claim 8, wherein said classification function may perform as at least one of: a regression function and a clustering function. 10. The system of claim 8, wherein said classification function is a CNN. 11. The system of claim 8, wherein said plurality of features is extracted by submitting said plurality of sample media objects to a CNN during a learning process of said CNN. 12. The system of claim 8, further comprising a topology of said classification function is optimized during said extraction of said plurality of features according to a size of each of said plurality of sample media objects. 13. The system of claim 8, wherein said augmentation includes applying at least one transformation to each of said plurality of features. 14. A computer program product for creating a classification function trained with augmented representations of features extracted from a plurality of sample media objects, comprising: a non-transitory computer readable storage medium; first program instructions to extract a plurality of features from a plurality of sample media objects; second program instructions to generate a plurality of features samples for each one of said plurality of features by augmenting said plurality of features; third program instructions to train a classification function with said plurality of features samples; and fourth program instructions to output said classification function for classifying at least one new media object; wherein said first, second, third and fourth program instructions are executed by at least one processor from said non-transitory computer readable storage medium. 15. The computer program product of claim 14, wherein said classification function may perform as at least one of: a regression function and a clustering function. 16. The computer program product of claim 14, wherein said classification function is a CNN. 17. The computer program product of claim 14, wherein said plurality of features is extracted by submitting said plurality of sample media objects to a CNN during a learning process of said CNN. 18. The computer program product of claim 14, further comprising a topology of said classification function is optimized during said extraction of said plurality of features according to a size of each of said plurality of sample media objects. 19. The computer program product of claim 14, wherein said augmentation includes applying at least one transformation to each of said plurality of features. 20. The computer program product of claim 19, further comprising said at least one transformation is a member selected from a group consisting of: a rotation, a shift, a translation, a scaling, a reflection, a geometric transformation, a pixel flip, a noise injection, a speed perturbation, a pitch shifting, a time stretching, a gain induction and/or a frequency spectrum warpage.