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With emerging demand for large-scale video analysis, MPEG initiated the compact descriptor for video analysis (CDVA) standardization in 2014. Beyond handcrafted descriptors adopted by the current MPEG-CDVA reference model, we study the problem of deep learned global descriptors for video matching, localization, and retrieval. First, inspired by a recent invariance theory, we propose a nested invariance...
Towards mobile visual search, compact visual descriptors have been well advocated in both academic and industry endeavors. Moving Picture Experts Group (MPEG) initiated the remarkable Compact Descriptors for Visual Search (CDVS) standard activity in Jan. 2010 to push forward the frontiers of compact descriptors in mobile internet industry. In Oct. 2014, MPEG CDVS successfully entered the Final Draft...
To ensure application interoperability in visual object search technologies, the MPEG Working Group has made great efforts in standardizing visual search technologies. Moreover, extraction and transmission of compact descriptors are valuable for next-generation, mobile, visual search applications. This article reviews the significant progress of MPEG Compact Descriptors for Visual Search (CDVS) in...
User-given tags associated with social images from photo-sharing websites (e.g., Flickr) are valuable auxiliary resources for the image tagging task. However, social images often suffer from noisy and incomplete tags, heavily degrading the effectiveness of previous image tagging approaches. To alleviate the problem, we introduce a Sparse Tag Patterns (STP) model to discover noiseless and complementary...
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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