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Information Processing and Management: an International Journal
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Image Based Localization in Urban Environments
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Inverted Index Compression for Scalable Image Matching
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PCA-SIFT: a more distinctive representation for local image descriptors
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The stanford mobile visual search data set
MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems
SURF: speeded up robust features
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IMShare: instantly sharing your mobile landmark images by search-based reconstruction
Proceedings of the 20th ACM international conference on Multimedia
Local visual words coding for low bit rate mobile visual search
Proceedings of the 20th ACM international conference on Multimedia
Heritage app: annotating images on mobile phones
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
A recursive embedding algorithm towards lossless 2D vector map watermarking
Digital Signal Processing
Robust and accurate mobile visual localization and its applications
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
Listen, look, and gotcha: instant video search with mobile phones by layered audio-video indexing
Proceedings of the 21st ACM international conference on Multimedia
Where should I stand? Learning based human position recommendation for mobile photographing
Multimedia Tools and Applications
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In this paper, we propose a multiple-channel coding scheme to extract compact visual descriptors for low bit rate mobile visual search. Different from previous visual search scenarios that send the query image, we make use of the ever growing mobile computational capability to directly extract compact visual descriptors at the mobile end. Meanwhile, stepping forward from the state-of-the-art compact descriptor extractions, we exploit the rich contextual cues at the mobile end (such as GPS tags for mobile visual search and 2D barcodes or RFID tags for mobile product search), together with the visual statistics at the reference database, to learn multiple coding channels. Therefore, we describe the query with one of many forms of high-dimensional visual signature, which is subsequently mapped to one or more channels and compressed. The compression function within each channel is learnt based on a novel robust PCA scheme, with specific consideration to preserve the retrieval ranking capability of the original signature. We have deployed our scheme on both iPhone4 and HTC DESIRE 7 to search ten million landmark images in a low bit rate setting. Quantitative comparisons to the state-of-the-arts demonstrate our significant advantages in descriptor compactness (with orders of magnitudes improvement) and retrieval mAP in mobile landmark, product, and CD/book cover search.