Learning apparatus, density measuring apparatus, learning method, computer program product, and density measuring system

摘要

A first extracting unit extracts partial images from a learning image. A first calculator calculates a feature amount of the partial image. A retrieving unit retrieves objects included in the partial image, and gives, as a label, a vector to the feature amount. The vector represents relative positions between a first position in the partial image and each object included in the partial image. A voting unit generates a voting histogram for each partial image. A learning unit divides the feature amount of each partial image into clusters to reduce variation of the corresponding voting histogram, so as to learn a regression model representing a relationship between the feature amount of the partial image and the relative position of the object included in the partial image. A first predicting unit predicts, for each cluster, a representative label from the label given to the feature amount belonging to the cluster.

主权项

1. A learning apparatus, comprising:
a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: first extracting unit a plurality of first partial images from a learning image; first calculating a feature amount of the first partial image; retrieving objects included in the first partial image, and gives, as a label, a vector to the feature amount, the vector representing relative positions between a first position in the first partial image and each of all objects included in the first partial image; calculating, for each of the plurality of first partial images, a histogram representing a distribution of the relative positions and votes the histogram into a parameter space so as to generate a voting histogram; dividing the feature amount corresponding to each of the plurality of first partial images into a plurality of clusters to reduce variation of the corresponding voting histogram, so as to learn a regression model representing a relationship between the feature amount of the first partial image and the relative position of the object included in the first partial image; and first predicting, for each of the plurality of clusters, a representative label from the label given to the feature amount that belongs to the cluster.