A system, method and computer product for predicting a content attribute are provided by collecting labeled media objects labeled with one or more attributes, constructing a feature vector of content-derived values for each of the labeled media objects, generating a model based on the feature vector and the one or more attributes, and applying the model to one or more unlabeled media objects to generate one or more attribute labels.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of predicting a content attribute, comprising the steps of: collecting one or more labeled media objects, wherein the one or more labeled media objects are labeled with one or more attributes; constructing a feature vector of one or more content-derived values for each of the one or more labeled media objects; generating a model based on the feature vector and the one or more attributes; and applying the model to one or more unlabeled media objects to generate one or more attribute labels. 2. The method according to claim 1, wherein the one or more labeled media objects are labeled based on a user input. 3. The method according to claim 1, wherein the one or more labeled media objects are labeled based on a title of the one or more labeled media objects. 4. The method according to claim 1, wherein the one or more labeled media objects are labeled based on a text description of the one or more labeled media objects. 5. The method according to claim 1, wherein the one or more attributes are a classification of the one or more media objects into one or more classes. 6. The method according to claim 1, wherein at least one of the attributes is a ranking of the one or more labeled media objects. 7. The method according to claim 1, wherein at least one of the content-derived values is normalized and scaled according to a distribution of the content-derived values in the one or more labeled media objects. 8. The method according to claim 1, wherein the step of constructing the feature vector further comprises steps of: sampling a segment of the one or more labeled media objects; identifying one or more local content features in the segment; clustering the one or more local content features into one or more clusters; normalizing and scaling the one or more clusters according to a distribution in the one or more labeled media objects; and concatenating the one or more clusters into the feature vector. 9. The method according to claim 1, wherein the model is calculated using a supervised learning model. 10. The method according to claim 9, wherein the supervised learning model is based on any one of (i) a logistic regression, (ii) support vector machines (SVM, or (iii) artificial neural networks (AAN). 11. The method according to claim 1, wherein the step of applying the model to the one or more unlabeled media objects further comprises steps of: estimating a predicted attribute of the one or more unlabeled media objects; and refining the predicted attribute using a probabilistic graphical model. 12. The method according to claim 11, wherein the probabilistic graphical model is a Bayesian network. 13. The method according to claim 1, wherein the steps are performed in a platform constructed to automatically label media content based on user activity. 14. A system comprising a processing unit adapted to perform: collecting one or more labeled media objects, wherein the one or more labeled media objects are labeled with one or more attributes; constructing a feature vector of one or more content-derived values for each of the one or more labeled media objects; generating a model based on the feature vector and the one or more attributes; and applying the model to one or more unlabeled media objects to generate one or more attribute labels. 15. The system according to claim 14, wherein the one or more labeled media objects are labeled based on a user input. 16. The system according to claim 14, wherein the one or more labeled media objects are labeled based on a title of the one or more labeled media objects. 17. The system according to claim 14, wherein the one or more labeled media objects are labeled based on a text description of the one or more labeled media objects. 18. The system according to claim 14, wherein the one or more attributes are a classification of the one or more media objects into one or more classes. 19. The system according to claim 14, wherein at least one of the attributes is a ranking of the one or more labeled media objects. 20. The system according to claim 14, wherein at least one of the content-derived values is normalized and scaled according to a distribution of the content-derived values in the one or more labeled media objects. 21. The system according to claim 14, wherein the step of constructing the feature vector further comprises steps of: sampling a segment of the one or more labeled media objects; identifying one or more local content features in the segment; clustering the one or more local content features into one or more clusters; normalizing and scaling the one or more clusters according to a distribution in the one or more labeled media objects; and and concatenating the one or more clusters into the feature vector. 22. The system according to claim 14, wherein the model is calculated using a supervised learning model. 23. The system according to claim 22, wherein the supervised learning model is based on any one of (i) a logistic regression, (ii) support vector machines (SVM or (iii) artificial neural networks (AAN). 24. The system according to claim 14, wherein the step of applying the model to the one or more unlabeled media objects further comprises steps of: estimating a predicted attribute of the one or more unlabeled media objects; and refining the predicted attribute using a probabilistic graphical model. 25. The system according to claim 24, wherein the probabilistic graphical model is a Bayesian network. 26. The system according to claim 14, wherein the steps are performed in a platform constructed to automatically label media content based on user activity. 27. A non-transitory computer-readable storage medium storing a program for causing a computer to implement the methods according to claim 1.