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Fisher vectors (FV) aggregated from local invariant features (e.g., SIFT) is one of the state-of-the-art descriptors for visual search, due to high discriminability but small visual vocabulary. Nevertheless, a high-dimensional FV needs to be compressed into a compact descriptor for light storage and high matching eficiency. In this paper, we formulate the FV compression as a resource-constrained optimization...
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...
Interest point detection is a fundamental approach to feature extraction in computer vision tasks. To handle the scale invariance, interest points usually work on the scale-space representation of an image. In this letter, we propose a novel block-wise scale-space representation to significantly reduce the computational complexity of an interest point detector. Laplacian of Gaussian (LoG) filtering...
In this paper, we propose a compact yet discriminative local descriptor which tackles the wireless query transmission latency in mobile visual search. The descriptor captures gradient statistics of canonical patches over a log-polar location grid whose parameters are optimized using training samples. We quantize the resulting descriptor using product quantization. The descriptor achieves about 95%...
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