Abstract: Provided is an image tagging device including: a first functional unit in which an image including an object that is a target of privacy protection is stored and that removes privacy information by changing part of the master image; a second functional unit that acquires the changed image from the first functional unit and changes a region image of an object that is not to be tagged in the first image; a function that distributes the changed image from the second functional unit to a tagging operation terminal device and receives image tag information from the tagging operation terminal device over a network; and a tagged image generator that generates a tagged image on the basis of the master image and the image tag information. This makes it possible to collect tagged images while achieving both privacy protection and an improvement in efficiency of a tagging operation.

Abstract: An image recognition method includes: receiving an image; acquiring processing result information including values of processing results of convolution processing at positions of a plurality of pixels that constitute the image by performing the convolution processing on the image by using different convolution filters; determining 1 feature for each of the positions of the plurality of pixels on the basis of the values of the processing results of the convolution processing at the positions of the plurality of pixels included in the processing result information and outputting the determined feature for each of the positions of the plurality of pixels; performing recognition processing on the basis of the determined feature for each of the positions of the plurality of pixels; and outputting recognition processing result information obtained by performing the recognition processing.

Abstract: Learning method includes performing a first process in which a coarse class classifier configured with a first neural network is made to classify a plurality of images given as a set of images each attached with a label indicating a detailed class into a plurality of coarse classes including a plurality of detailed classes and is then made to learn a first feature that is a feature common in each of the coarse classes, and performing a second process in which a detailed class classifier, configured with a second neural network that is the same in terms of layers other than the final layer as but different in terms of the final layer from the first neural network made to perform the learning in the first process, is made to classify the set of images into detailed classes and learn a second feature of each detailed class.