Analysis of the Effect of Dataset Differences on Object Recognition: The Case Of Recognition Methods Based on Exact Matching of Feature Vectors

摘要

Specific object recognition methods based on the exact matching of feature vectors are known as methods that can achieve high recognition performance for large-scale three-dimensional specific object recognition. Since there are few common three-dimensional object datasets whose size is sufficient to explore the effect of differences in object dataset composition and the effect of increasing number of objects for recognition, these effects for specific object recognition methods based on exact matching of feature vectors have been discussed insufficiently. The number of objects in well-known datasets (e.g., COIL-100) is around 100. Therefore, in this research, we prepared a dataset of 1002 three-dimensional objects by themselves. In this paper, we discuss the effect of dataset differences, which are based on object structure, texture, and the number of objects, for methods such as that based on the Bloomier filter and that based on a hash table with this dataset in addition to COIL-100.