Low-rate image retrieval with tree histogram coding
Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
Location coding for mobile image retrieval
Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
Towards low bit rate mobile visual search with multiple-channel coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Location Discriminative Vocabulary Coding for Mobile Landmark Search
International Journal of Computer Vision
Learning compact visual descriptor for low bit rate mobile landmark search
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Energy Conservation for Image Retrieval on Mobile Systems
ACM Transactions on Embedded Computing Systems (TECS)
Local visual words coding for low bit rate mobile visual search
Proceedings of the 20th ACM international conference on Multimedia
A low-bandwidth camera sensor platform with applications in smart camera networks
ACM Transactions on Sensor Networks (TOSN)
Heritage app: annotating images on mobile phones
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Learning from mobile contexts to minimize the mobile location search latency
Image Communication
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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 5x rate reduction compared to sending compressed feature descriptors. By discarding the order amongst a list of features, histogram coding requires 1.5x 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.