  The compact descriptors for visual search (CDVS) standard from ISO/IEC moving
pictures experts group (MPEG) has succeeded in enabling the interoperability
for efficient and effective image retrieval by standardizing the bitstream
syntax of compact feature descriptors. However, the intensive computation of
CDVS encoder unfortunately hinders its widely deployment in industry for
large-scale visual search. In this paper, we revisit the merits of low
complexity design of CDVS core techniques and present a very fast CDVS encoder
by leveraging the massive parallel execution resources of GPU. We elegantly
shift the computation-intensive and parallel-friendly modules to the
state-of-the-arts GPU platforms, in which the thread block allocation and the
memory access are jointly optimized to eliminate performance loss. In addition,
those operations with heavy data dependence are allocated to CPU to resolve the
extra but non-necessary computation burden for GPU. Furthermore, we have
demonstrated the proposed fast CDVS encoder can work well with those
convolution neural network approaches which has harmoniously leveraged the
advantages of GPU platforms, and yielded significant performance improvements.
Comprehensive experimental results over benchmarks are evaluated, which has
shown that the fast CDVS encoder using GPU-CPU hybrid computing is promising
for scalable visual search.
