CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of Japanese Patent Application No. 2019-010257, filed on Jan. 24, 2019, and Japanese Patent Application No. 2019-217687, file on Dec. 2, 2019, the entire disclosure of which is incorporated by reference herein.

FIELD

This application relates generally to an image searching apparatus, a classifier training method, and a recording medium.

BACKGROUND

Techniques have been developed for classifying images into predetermined classes by using a convolutional neural network (CNN)-based classifier. For example, with use of such a technique, an image (dermoscopic image) of a skin-disease affected area, taken by a device such as a dermatoscope, is classified into a class for which a disease name is defined, and thus the skin disease name of the affected area can be presumed. When doing so, often is the case that it is more convenient to display the images in a ranked manner in accordance with the degree of similarity of the classes, instead of merely classifying the images. For example, “Deep Metric Learning Using Triplet Network”, [online], Dec. 4, 2018, [Searched Jan. 8, 2019] on the Internet (URL: https://arxiv.org/pdf/1412.6622.pdf), authored by Elad Hoffer and Nir Ailon, discloses a training method for parameters that create features where classes that are the same are made to have short distances therebetween, whereas classes that are different from each other are made to have long distances therebetween. In doing so, image searching system can be created where images that are of the same class are given a high ranking and images of different classes are given a low ranking.

SUMMARY

An image searching apparatus includes:

a processor; and

a memory configured to store a program to be executed by the processor,

wherein the processor is configured toattach, to an image with a first correct label attached thereto, a second correct label, the first correct label being a correct label attached to each image included in an image dataset for training for use in supervised training, the second correct label being a correct label based on a degree of similarity from a predetermined standpoint,execute main training processing to train a classifier by using (i) the images to which the first correct label is attached or the first correct label and the second correct label are attached and (ii) the attached first correct label or one of the attached first correct label and the attached second correct label,fine-tune a training state of the classifier, trained by the main training processing, by using (i) the images to which the first correct label and the second correct label are attached and (ii) the other one of the attached first correct label and the attached second correct label, andsearch, by using the classifier that is fine-tuned, for images similar to a query image received from a user.

