US 11,790,635 B2
Learning device, search device, learning method, search method, learning program, and search program
Yukito Watanabe, Tokyo (JP); Takayuki Umeda, Tokyo (JP); Jun Shimamura, Tokyo (JP); and Atsushi Sagata, Tokyo (JP)
Assigned to NIPPON TELEGRAPH AND TELEPHONE CORPORATION, Tokyo (JP)
Appl. No. 17/619,239
Filed by NIPPON TELEGRAPH AND TELEPHONE CORPORATION, Tokyo (JP)
PCT Filed Jun. 17, 2019, PCT No. PCT/JP2019/023976
§ 371(c)(1), (2) Date Dec. 14, 2021,
PCT Pub. No. WO2020/255227, PCT Pub. Date Dec. 24, 2020.
Prior Publication US 2022/0284695 A1, Sep. 8, 2022
Int. Cl. G06V 10/74 (2022.01); G06F 16/532 (2019.01); G06F 16/56 (2019.01); G06V 10/778 (2022.01); G06V 10/40 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/761 (2022.01) [G06F 16/532 (2019.01); G06F 16/56 (2019.01); G06V 10/40 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01)] 12 Claims
OG exemplary drawing
 
2. A learning apparatus comprising circuitry configured to execute operations comprising:
calculating a second similarity for a combination of reference images by using information on features different from feature vectors of labeled reference images, the second similarity being calculated as a similarity determined using the information on the features,
the reference images including a basic image as a reference of the labeling, a similar image that is the reference image similar to the basic image, and a dissimilar image that is the reference image dissimilar to the basic image; and
updating a parameter of a neural network such that a margin increases as the second similarity between the basic image and the dissimilar image increases relative to a second similarity between the basic image and the similar image, the parameter being updated by using a loss function including a first similarity between a feature vector of the basic image and a feature vector of the similar image, a first similarity between the feature vector of the basic image and a feature vector of the dissimilar image, and the margin based on the second similarity between the basic image and the similar image and the second similarity between the basic image and the dissimilar image, the feature vectors being outputted from the neural network for receiving a predetermined image and outputting the feature vectors.