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Smart phones is bringing about emerging potentials in mobile visual search. Extensive research efforts have been made in compact visual descriptors. However, directly extracting visual descriptors on a mobile device is computationally intensive and time consuming. Towards low bit rate visual search, we propose to deeply compress query images by learning a customized JPEG quantization table in the...
Compressing a query image's signature via vocabulary coding is an effective approach to low bit rate mobile visual search. State-of-the-art methods concentrate on offline learning a codebook from an initial large vocabulary. Over a large heterogeneous reference database, learning a single codebook may not suffice for maximally removing redundant codewords for vocabulary based compact descriptor. In...
We propose a novel locality sensitive vocabulary coding scheme to extract compact descriptors for low bit rate visual search. We employ Latent Dirichlet Allocation (LDA) to learn the topic vocabularies of lower dimension to generate compact descriptors. To deal with diverse datasets, LDA model is introduced to subdivide a dataset into groups of images with a topic model, where the code word distributions...
State-of-the-art mobile visual search systems put emphasis on developing compact visual descriptors [4][6], which enables low bit rate wireless transmission instead of delivering an entire query image. In this paper, we address the orderless nature of the transmission set of query descriptors . We propose to adapt the orders of local descriptors in transmission, which subsequently yields more consistent...
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