Unsupervised adaptation method and automatic image classification method applying the same

摘要

An automatic image classification method applying an unsupervised adaptation method is provided. A plurality of non-manually-labeled observation data are grouped into a plurality of groups. A respectively hypothesis label is set to each of the groups according to a classifier. It is determined whether each member of the observation data in each of the groups is suitable for adjusting the classifier according to the hypothesis label, and the non-manually-labeled observation data which are determined as being suitable for adjusting the classifier are set as a plurality of adaptation data. The classifier is updated according to the hypothesis label and the adaptation data. The observation data are classified according to the updated classifier.

主权项

1. An unsupervised adaptation method applied to an electronic device, comprising:
implementing a processor to perform the following steps; grouping a plurality of non-manually-labeled observation data into a plurality of groups; setting a respective hypothesis label to each of the groups according to a classifier; obtaining a shortest distance between each member of the observation data of each of the groups and a plurality of the members of the sample set of the hypothesis label, and obtaining a minimum of a plurality of distances between the member of the observation data and a plurality of members of representation sets of other hypothesis labels; determining whether each member of the observation data in the groups is suitable for adjusting the classifier according to a ratio of a shortest distance between the observation data and the hypothesis label to a minimum of a plurality of distances between the observation data and other hypothesis labels, and setting the observation data which are determined as being suitable for adjusting the classifier as a plurality of adaptation data; predicting at least one adjustment parameter of the classifier according to the hypothesis labels and the adaptation data, to adjust the classifier; and iterating the above steps to adjust the classifier.