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.
2. The apparatus according to claim 1 , further comprising adapting a target-class weight for the first likelihood of the target class and selected-class weights for the first likelihoods of the selected classes in a manner that the target-class weight is larger than any of the selected-class weight, and applying the target-class weight with the first likelihood of the target class to obtain a third value and apply the selected-class weights on the first likelihoods of the selected classes to obtain a fourth value, and sum up the third value and the fourth value to obtain the second likelihood.