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Visual patterns, i.e., high-order combinations of visual words, contributes to a discriminative abstraction of the high-dimensional bag-of-words image representation. However, the existing visual patterns are built upon the 2D photographic concurrences of visual words, which is ill-posed comparing with their real-world 3D concurrences, since the words from different objects or different depth might...
In this paper, we present the state-of-the-art compact descriptors for mobile visual search. In particular, we introduce our MPEG contributions in global descriptor aggregation and local descriptor compression, which have been adopted by the ongoing MPEG standardization of compact descriptor for visual search (CDVS). Standardization progress will be introduced. Other issues including visual object...
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...
We introduce a Compact Topical Descriptor to learn a compact yet discriminative image signature from the reference image corpus. This descriptor is deployed over the well used bag-of-words image histogram, with two merits over the traditional topical features: First, we propose to directly control the topical sparsity to achieve the descriptor compactness. Second, we ensure the descriptor discriminability...
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...
We present a low bit rate vocabulary coding scheme in the context of mobile landmark search. Our scheme exploits location cues to boost a compact subset of visual vocabulary, which is discriminative for visual search and incurs low bit rate query for efficient upstream wireless transmission. To validate the coding scheme, we have developed mobile landmark search prototype systems within typical areas...
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