Asymmetric Feature Representation for Object Recognition in Client Server System

Abstract

This paper proposes asymmetric feature representation and efficient fitting feature spaces for object recognition in client server system. We focus on the fact that the server-side has more sufficient memory and computation power compared to the client-side. Although local descriptors must be compressed on the client-side due to the narrow bandwidth of the Internet, feature vector compression on the server-side is not always necessary. Therefore, we propose asymmetric feature representation for descriptor matching. Our method is characterized by the following three factors. The first is asymmetric feature representation between client- and server-side. Although the binary hashing function causes quantization errors due to the computation of the sgn function $(\cdot )$, which binarizes a real value into $\{1,-1\}$, such errors only occur on the client-side. As a result, performance degradation is suppressed while the volume of data traffic is reduced. The second is scale optimization to fit two different feature spaces. The third is fast implementation of distance computation based on real-vector decomposition. We can compute efficiently the squared Euclidean distance between the binary code and the real vector. Experimental results revealed that the proposed method helps reduce data traffic while maintaining the object retrieval performance of a client server system.