## Distance Computations between Binary Code and Real-Number Vectors for Efficient Keypoint Matching

An issue that has arisen in the implementation of object recognition in a client/server system is the reduction of the amount of data transfer received by the server from the client, in order to lessen the load on the network. However, since a reduction in the data transfer amount leads to a drop in performance, there is a desire for a reduction in the data transfer amount while maintaining the recognition performance. We therefore propose high-speed distance calculations by local features represented by binary code on the client side and features represented by real-number vectors on the server side.

**Matching of Feature Points Based on Distances between Binary Code and Real-Number Vectors**

The proposed method represents local features as real-number vectors on the server and as binary code on the client. It calculates the Euclidean distances between the binary code and real-number vectors, and matches feature points on the basis of distance. However, there is a problem in that, if the magnitudes of the Euclidean norm of the binary code and the real-number vector is radically different, the accuracy of feature point matching will drop. In such a case, we introduce a scale factor that absorbs the disparity in the Euclidean norm of the binary code and the real-number vector, and implement highly accurate calculations of the distance between feature point by optimization.

**High-Speed Euclidean Distance Calculations Enabled by Introduction of Vector Resolution Method**

We can expect an improvement in the matching accuracy between feature points by maintaining the feature vectors on the server as highly representational real numbers. A great reduction in the amount of calculations and the amount of memory used can be implemented, in a situation in which the matching performance of feature points is maintained, by resolving the real-number vectors on the server side into a small number of weight factors and binary basis vectors. The distance calculations can be done approximately eight times faster and the memory amount can be reduced to approximately 1/17 by resolving the real-number vectors on the server side.