Estimating apparatus, method thereof, and computer program product therefor

摘要

An estimating apparatus configured to estimate a correct attribute value is provided. The estimating apparatus extracts feature quantities from an image including a person, calculates a first likelihood of the feature quantity for respective attribute classes; calculating second likelihoods for the respective attribute classes from the first likelihoods for the respective attribute classes; specifies the attribute class having the highest second likelihood; calculates an estimated attribute value of the specific attribute class and estimated attribute values of selected classes by using the feature quantity; and applies the second likelihood on the estimated attribute value of the specific attribute class as a weight, applies the second likelihoods on the estimated attribute values of the selected classes as a weight and add the same, and calculates a corrected attribute value of the specific attribute class.

主权项

1. An estimating apparatus comprising:
a memory that stores computer executable instructions; and a processor, coupled to the memory, that executes the computer executable instructions to perform operations, comprising: acquiring an image; extracting human feature quantity from the image; calculating, from the feature quantity, a first likelihood which indicates a degree of likelihood that the feature quantity belongs to for each of attribute classes, which comprises segments of consecutive attribute values relating to a person; designating one of the attribute classes as a target class, designating two or more of the attribute classes near the target class as selected, and summing up the first likelihood of the target class and the first likelihoods of selected classes to obtain the second likelihood of the target class; specifying one of the attribute classes as a specific attribute class, which has the highest second likelihood, from among the second likelihoods respectively for the attribute classes; calculating an estimated attribute value of the specific attribute class and estimated attribute values of the selected classes by setting the specific attribute class as the target class, respectively by using the feature quantity; and applying the second likelihood of the specific attribute class on the estimated attribute value of the specific attribute class as a weight to obtain a first value, applying the second likelihoods of the selected classes respectively on the estimated attribute values of the selected classes as weights to obtain a second value, and summing up the first value and the second value to obtain a corrected attribute value of the specific attribute class.