Patent Document ID: 8737771
Application ID: 13005062

Base Claim:
1. An annotation addition method of adding one or more annotations into an input medium file, comprising: an annotation detection model creation step of creating one or more annotation detection models based on one or more training samples formed by one or more existing media files having one or more annotations; an annotation coexistence coefficient extraction step of extracting one or more coexistence coefficients of any two annotations based on appearance frequencies of the annotations in the training samples; a medium file input step of inputting the input medium file; a sense-of-vision feature extraction step of extracting one or more sense-of-vision features from the input medium file, the sense of vision features which are extracted include a low level feature including at least a Fourier description of the input medium file, and a high level feature including at least a creation environment of the input medium file; an initial annotation obtaining step of obtaining one or more initial annotations of the input medium file; a candidate annotation acquiring step of acquiring one or more candidate annotations based on the initial annotations and the coexistence coefficients of the annotations in the training samples, wherein the candidate annotation acquiring step includes: determining, for each of the one or more training samples, coexistence frequencies of any two annotations in a same one of the one or more existing media files; calculating a number of the existing media files which include at least one of the annotation; and acquiring annotation coexistence coefficients of any two annotations, based on the number of the existing media files which has been calculated; and a final annotation set selection step of selecting a final annotation set suitable for describing content of the input medium file from the candidate annotations based on the sense-of-vision features of the input medium file and the coexistence coefficients by using the annotation detection models.

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Claim 7:
7. The annotation addition method according to claim 1 , wherein, in a case where the number of the initial annotations is plural, the candidate annotation acquiring step comprises: a step of acquiring candidate annotation lists corresponding to the respective initial annotations; and a step of selecting one or more final candidate annotation lists able to describe the content of the input medium file from the candidate annotation lists, including: a step of acquiring, by using the annotation detection models, a degree of confidence of existence of each of the candidate annotations in each of the candidate annotation lists with regard to the input medium file; a step of acquiring, based on coexistence coefficients of the candidate annotations in each of the candidate annotation lists, a coexistence coefficient matrix of the candidate annotations in the corresponding candidate annotation list, wherein, the dimensions of the coexistence coefficient matrix are equal to the number of the candidate annotations in the corresponding candidate annotation list; a step of calculating, based on the degree of confidence of existence of each of the candidate annotations in the corresponding candidate annotation list and the coexistence coefficient matrix of the candidate annotations in the corresponding candidate annotation list, existence scores of the respective annotations in the corresponding candidate annotation list with regard to the input medium file, and then, by totaling the existence scores of the respective annotations in the corresponding candidate annotation list with regard to the input medium file, acquiring a combined existence score of the corresponding candidate annotation list, wherein, the higher the combined existence score is, the more accurately the corresponding candidate annotation list describes the content of the input medium file; a step of ranking, based on each of the combined existence scores of the candidate annotation lists, the combined existence scores of the candidate annotation lists; and a step of selecting, based on the ranked result, one or more final candidate annotation lists able to describe the content of the input medium file.