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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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    Area of Science:

    • Computer Vision
    • Image Processing
    • Multimedia Systems

    Background:

    • The Compact Descriptors for Visual Search (CDVS) standard enables interoperable image retrieval.
    • High computational demands of CDVS encoders limit widespread industrial adoption for large-scale visual search.

    Purpose of the Study:

    • To develop a significantly faster CDVS encoder by leveraging GPU parallel processing.
    • To optimize CDVS encoding for efficient and scalable visual search applications.

    Main Methods:

    • Utilizing graphics processing units (GPUs) for computation-intensive and parallel-friendly CDVS modules.
    • Jointly optimizing thread block allocation and memory access on GPUs.
    • Allocating data-dependent operations to the CPU to offload GPU.
    • Integrating the CDVS encoder with Convolutional Neural Network (CNN) approaches.

    Main Results:

    • Demonstrated significant performance improvements through GPU-CPU hybrid computing.
    • Achieved a very fast CDVS encoder, overcoming previous computational bottlenecks.
    • Showcased harmonious integration with CNNs on GPU platforms.

    Conclusions:

    • The proposed fast CDVS encoder using GPU-CPU hybrid computing is highly promising for scalable visual search.
    • This approach effectively addresses the computational challenges of CDVS for practical, large-scale applications.