Tree Histogram Coding for Mobile Image Matching

Abstract

For mobile image matching applications, a mobile device captures a query image, extracts descriptive features, and transmits these features wirelessly to a server. The server recognizes the query image by comparing the extracted features to its database and returns information associated with the recognition result. For slow links, query feature compression is crucial for low-latency retrieval. Previous image retrieval systems transmit compressed feature descriptors, which is well suited for pairwise image matching. For fast retrieval from large databases, however, scalable vocabulary trees are commonly employed. In this paper, we propose a rate-efficient codec designed for tree-based retrieval. By encoding a tree histogram, our codec can achieve a more than 5times rate reduction compared to sending compressed feature descriptors. By discarding the order amongst a list of features, histogram coding requires 1.5times lower rate than sending a tree node index for every feature. A statistical analysis is performed to study how the entropy of encoded symbols varies with tree depth and the number of features.